A Longitudinal Analysis of Passenger Travel Disruptions in
the National Air Transportation System
by
Esther Nyokabi Njuguna
B.S., Aerospace Management: Aviation Business, Mathematics
Averett University, 2012
Submitted to the Department of Civil and Environmental Engineering Partial Fulfillment of the
Requirements for the Degree of
Master of Science in Transportation
MASSACHUSETTS NSTiTUTE
OF TECHNOLOGY
at the
of Technology
Institute
Massachusetts
JUN 13 2014
June 2014
L BRARIES
@ 2014 Massachusetts Institute of Technology. All Rights Reserved.
Signature of Author:
Signature redacted
Department of Civil and Environmental Engineering
May 22, 2014
Certified by:
Signature redacted
Cynthia Barnhart
Chancellor
Ford Professor of Engineering
Thesis Supervisor
Signature redacted
Certified by:
Vikrant Vaze
Assistant Professor of Engineering
Thayer School of Engineering, Dartmouth College
Aiesis Super isor
Accepted by:
Signature redacted_
I
I
Heidi M. 4pf
Chair, Departmental Committee for Graduate Students
A Longitudinal Analysis of Passenger Travel Disruptions in the National
Air Transportation System
by
Esther Nyokabi Njuguna
Submitted to the Department of Civil and Environmental Engineering on
May 22, 2014, in partial fulfillment of the requirements for the degree of
Master of Science in Transportation
Abstract
Recent research on the U.S. National Air Transportation System has shown that
approximately 50% of delays suffered by passengers are a result of passenger travel
disruptions in the form of either flight cancellations or missed connections. There exists
significant variation in the propensity for disruptions across airports and carriers, based
on key factors such as scheduling practices, network structures, and passenger
connections. In this thesis, we conduct a longitudinal analysis of passenger travel
disruptions in the U.S. over the 2006 - 2010 calendar years and analyze the trends in,
and impacts of, various scheduling and operational factors across the years. We
illustrate the interdependencies of carrier-related factors and airport-related factors, and
the resulting impact on passenger travel disruptions. In our analysis, we use passenger
travel data spanning five years. This passenger travel data is estimated from publicly
available data sources using a methodology previously developed to disaggregate
passenger demand data. We find that across the years, flight cancellations, which are
the leading cause for passenger disruptions, vary substantially across carriers even
when baseline variability across airports is accounted for. Both passenger and
operational considerations play a very significant role in cancellation decisions. We
explore the effect of load factors and flight frequency on cancellation rates across the
carriers and determine that the level of impact of these two factors varies across carrier
types, with the cancellation decisions among the regional carriers being influenced
more by load factor considerations than by flight frequency.
Much of the variability in missed connections at the airport and carrier level can be
explained by the flight delays observed. However, an airline's scheduling practices are
also a critical factor that affects the rate of misconnections. Highly banked (peaked)
flight schedules reduce passenger connection times and result in higher misconnection
rates.
We find that significant trends and events in the aviation industry, including the
Valentine's Day Storm of February 14, 2007, the demise of Aloha Airlines in 2008, the
3
trend towards capacity discipline among legacy carriers after 2008, and Delta and
Northwest Airlines' merger in 2010 have had a significant impact on both cancellations
and misconnections.
Thesis Supervisor: Cynthia Barnhart
Title: Chancellor
Ford Professor of Engineering
Thesis Supervisor: Vikrant Vaze
Title: Assistant Professor of Engineering
Thayer School of Engineering, Dartmouth
4
Acknowledgments
First and foremost, I thank the Lord God Almighty for this humbling opportunity; for
His sustaining grace through my entire time at MIT, His providence, and for renewing
my strength to the very end. Without Him, none of this would have been possible.
I am extremely grateful for the support, guidance, and sacrifice that I have received
from my two supervisors, Chancellor Cynthia Barnhart, and Prof. Vikrant Vaze. Cindy
has been a constant source of encouragement, knowledge, and wisdom. She has been a
great mentor, not only in my research and thesis, but also in my personal life - her zeal,
strong work ethics, patience, and compassion towards her students truly make her one
of the best advisors and I feel honored to have had the opportunity to work with her. I
am greatly indebted to Vikrant for his immense support and guidance. He worked
tirelessly, offering insurmountable advice, assistance, and instruction throughout.
Vikrant offered encouragement and support to help overcome the many obstacles we
encountered along the way, and provided many novel solutions to challenging issues.
This thesis has greatly benefited from Cindy's and Vikrant's immense knowledge,
expertise, and understanding of the aviation industry.
Most importantly, I would like to thank my family. The constant love, encouragement,
and support that I received from my mother, Mary, my brother, Victor enabled me to
make it through.
5;
6
Contents
A bstract ..........................................................................................................................................
4
1 Introduction ............................................................................................................................
13
1.1 D ata Sources ......................................................................................................................
14
1.2 Thesis Contributions......................................................................................................
15
1.3 Thesis Outline ....................................................................................................................
15
2 Analysis of Flight Cancellations over the 2006 - 2010 Time Period.........................17
2.1 A irports and Carriers ..........................................................................................
17
2.2 Flight Frequency and Load Factors....................................................................
35
2.2.1 Regional Carriers A nalysis ...................................................................
38
2.3 Carrier Effect..............................................................................................................57
2.4 M odelling Cancellations .....................................................................................
79
3 Analysis of Missed Connections over the 2006 - 2010 Time Period .......................
94
3.1 M issed C onnections at the A irport-Specific Level...........................................
94
3.2 M issed C onnections at the Carrier-Specific Level..............................................104
3.3 Schedule Banking....................................................................................................118
3.4 M odelling M issed Connections ............................................................................
123
4 C onclusions and Further Research ...................................................................................
131
A ppendix A ...............................................................................................................................
134
7
List of Figures
2.1.1 Cancellation rates for the top 50 busiest airports in 2006 .......................................
19
2.1.2 Cancellation rates for the top 50 busiest airports in 2007 .......................................
20
2.1.3 Cancellation rates for the top 50 busiest airports in 2008 .......................................
22
2.1.4 Cancellation rates for the top 50 busiest airports in 2009 .......................................
24
2.1.5 Cancellation rates for the top 50 busiest airports in 2010 .......................................
25
2.1.6 Cancellation rates for each carrier group across the 2006 - 2010 time period .....
28
2.1.7 Cancellation rates for each carrier in 2006.................................................................29
2.1.8 Cancellation rates for each carrier in 2007.................................................................
30
2.1.9 Cancellation rates for each carrier in 2008.................................................................
32
2.1.10 Cancellation rates for each carrier in 2009...............................................................33
2.1.11 Cancellation rates for each carrier in 2010...............................................................34
2.1.12 Average distribution of flight departures at LGA and ORD over the 2006 - 2010
tim e p e rio d ...................................................................................................................................
35
2.2.1 Visual presentation of the percentage of regional carriers' carrier-segments
operated on behalf of corresponding legacy carriers .......................................................
42
2.2.2 Correlation between average flight frequency and average flight cancellation rates
for D L, 9ED L, and 9EnonD L ................................................................................................
44
2.2.3 Correlation between average flight frequency and average flight cancellation rates
for D L, EV D L, and EV nonD L...............................................................................................
45
2.2.4 Correlation between average flight frequency and average flight cancellation rates
for AA , M Q A A, and M QnonA A .........................................................................................
46
2.2.5 Correlation between average flight frequency and average flight cancellation rates
for D L, O H D L, and O HnonD L ............................................................................................
47
2.2.6 Correlation between average flight frequency and average flight cancellation rates
for D L, O O D L, and OO nonD L...............................................................................................
48
2.2.7 Correlation between average flight frequency and average flight cancellation rates
for U A , O OU A , and O O nonU A ............................................................................................
49
2.2.8 Correlation between average flight frequency and average flight cancellation rates
for X E, X EC O, and X EnonC O ..............................................................................................
50
2.2.9 Correlation between average flight frequency and average flight cancellation rates
for U S, YV U S, and YV nonU S ................................................................................................
51
8
2.2.10 Correlation between average flight frequency and average flight cancellation
rates for U A , YVU A , and YVnonU A ....................................................................................
52
2.3.1 Coefficient of hubbing values for FL and DL ............................................................
72
3.1.1 Average misconnection rates across the top 50 busiest airports in 2006 ..............
95
3.1.2 Average misconnection rates across the top 50 busiest airports in 2007...............96
3.1.3 Average misconnection rates across the top 50 busiest airports in 2008 ................. 100
3.1.4 Average misconnection rates across the top 50 busiest airports in 2009.................101
3.1.5 Average misconnection rates across the top 50 busiest airports in 2010 ................. 103
3.2.1 Misconnection rates for the four carrier groups across the 2006 - 2010 time
p e rio d .........................................................................................................................................
105
3.2.2 Misconnection rates across the carriers in 2006...........................................................106
3.2.3 Misconnection rates across the carriers in 2007...........................................................108
3.2.4 M isconnection rates across the carriers in 2008...........................................................110
3.2.5 Misconnection rates across the carriers in 2009...........................................................112
3.2.6 Misconnection rates across the carriers in 2010...........................................................114
3.3.1 N W 's banked schedule at M EM in 2007.......................................................................119
3.3.2 AA's de-banked schedule at ORD in 2007 ...................................................................
3.3.3 D L's banked schedule at JFK in 2007............................................................................120
3.3.4 DL's de-banked schedule at ATL in 2007.....................................................................121
9
119
List of Tables
2.1.1 Range of values within which the true mean should fall .......................................
18
2.1.2 Month-by-month cancellation rates at JFK in 2007...................................................21
2.1.3 Month-by-month cancellation rates for B6 in 2007...................................................31
2.2.1 Correlation between average flight frequency and flight cancellation rates across
the four carrier groups over the 2006 - 2010 time period ................................................
37
2.2.2 Regional carriers, legacy carrier they serve, and the hubs they operate out of ........ 40
2.2.3 Percentage of regional carriers' carrier-segments operated on behalf of
corresponding legacy carriers ..............................................................................................
41
2.2.4 Adjusted R-square values for the three models for the legacies.............................55
2.2.5 Adjusted R-square values for the three models for the regional carriers..............56
2.3.1 C arrier effects in 2006 ...................................................................................................
59
2.3.2 C arrier effects in 2007 ...................................................................................................
60
2.3.3 C arrier effects in 2008 ...................................................................................................
62
2.3.4 Carrier effects in 2009 ...................................................................................................
64
2.3.5 Carrier effects in 2010 ...................................................................................................
65
2.3.6 Effects of primary hub on cancellation rates in 2006 ..............................................
67
2.3.7 Effects of primary hub on cancellation rates in 2007...............................................
68
2.3.8 Effects of primary hub on cancellation rates in 2008 ..............................................
69
2.3.9 Effects of primary hub on cancellation rates in 2009 ..............................................
70
2.3.10 Effects of primary hub on cancellation rates in 2010 ..............................................
71
2.3.11 Cancellation rates and large delays for flight arrivals and departures at the
p rim ary h u b in 2006....................................................................................................................73
2.3.12 Cancellation rates and large delays for flight arrivals and departures at the
p rim ary h u b in 2007....................................................................................................................75
2.3.13 Cancellation rates and large delays for flight arrivals and departures at the
p rim ary h u b in 2008 ....................................................................................................................
76
2.3.14 Cancellation rates and large delays for flight arrivals and departures at the
p rim ary h u b in 2009 ....................................................................................................................
77
2.3.15 Cancellation rates and large delays for flight arrivals and departures at the
p rim ary h u b in 2010 ....................................................................................................................
78
10
2.4.1 Estimation results for model 1 (airport congestion) for the 2006 - 2010 time
p e rio d .....................................................................................................
8 1 -8 2
2.4.2 Distribution of on-time performance across the years .............................................
83
2.4.3 Estimation results for model 2 (carrier types) for the 2006 - 2010 time period.. .83-85
2.4.4 Estimation results for model 3 (combination of airport congestion and carrier
types) for the 2006 - 2010 tim e period ...............................................................................
86-88
2.4.5 Estimation results for model 4 (combination of airport congestion, carrier types,
frequency, load factor, and origin hub dummy) for the 2006 - 2010 time period.......89-92
3.1.1 Average connection times by hour at IND and distribution of connecting
passengers in 2006 and 2007.................................................................................................
98
3.2.1 D L's m isconnection rates at N W 's hubs.......................................................................115
3.2.2 Multi- and same-carrier itineraries across the carrier groups ...................................
116
3.2.3 Percentage of multi- and same-carrier itineraries across the carrier groups .......... 117
3.3.1 Peak index values and connection times for the NW, AA, and DL examples........121
3.3.2 Average flight delay and average connection time at the five airports with
consistently high m isconnection rates ...................................................................................
122
3.3.3 Average peak index values, average connection time, and average share of
connecting passengers at IA D and JFK ..................................................................................
123
3.4.1 Estimation results for the regression models for 2006................................................125
3.4.2 Estimation results for the regression models for 2007................................................126
3.4.3 Estimation results for the regression models for 2008................................................127
3.4.4 Estimation results for the regression models for 2009................................................128
3.4.5 Estimation results for the regression models for 2010................................................129
A .1 C arrier nam es and IA TA codes .......................................................................................
A .2 A irport nam es and IA TA codes.......................................................................................137
11
136
12
Chapter 1
Introduction
Flight delays cause a huge strain on the U.S. air transportation system, and have a
significant impact on the U.S. economy as a whole. In 2007, which was the last full year
of peak air travel demand prior to the economic downturn that began in 2008, the U.S.
air transportation system experienced the worst year in terms of flight delays, with 24%
of all domestic flights experiencing more than 15 minutes of delay and 2% of all
domestic flights being cancelled (CAPA - Center of Aviation, 2010). The sum of the
number of flights delayed for more than 15 minutes and the number of flight
cancellations increased by 62% between 1998 and 2007 (CAPA - Center for Aviation,
2010). For this reason, the Federal Aviation Administration (FAA) commissioned a
comprehensive study (Ball et al., 2010) of the total delay impact (TDI) on airlines,
passengers, and the U.S. economy as a whole. According to the results of the study, the
total cost of delays within the U.S. air transportation system was estimated at $31.2
billion. Of this $31.2 billion, the total cost of delays to airlines in 2007 was estimated at
$8.3 billion, most of which consisted of increased expenses for labor, fuel, and
maintenance (Ball et al., 2010). It was estimated that passengers bore the largest share of
the cost of delay, with the passenger time lost due to schedule buffer, delayed flights,
flight cancellations and misconnections in 2007 estimated at $16.7 billion. The cost of
lost demand for air travel in 2007 was estimated at $2.2 billion. Moreover, flight delays
that year were estimated to have reduced the U.S. GDP by $4 billion. According to the
TDI report, it is estimated that in 2007, passengers experienced approximately 458.1
million hours of delay due to airline schedule buffers, delays to flights, capacityinduced delay, and voluntary schedule adjustments (Ball et al., 2010).
According to a report by the Center for Air Transportation Systems Research at George
Mason University on passenger delay for 2008, passengers experienced delays due to
flights that had been delayed, cancelled, diverted, and oversold (CATSR, 2008). The
report goes on to explain that in 2008, there was a 5% decrease in passenger trips and a
6% decrease in airline flights from 2007, due to the economic downturn that began that
year. It is estimated that the delay suffered by passengers decreased by 10% from 2007,
to a total of 299 million hours in 2008. These passenger hours of delay were estimated to
cost the U.S. economy more than $8.9 billion in lost productivity that year. Furthermore,
Barnhart, Fearing, and Vaze (2013) use a regression-based approach to estimate the total
delay experienced by passengers in 2008 to be about 204 million hours, which was 12%
lower than the total delay experienced by passengers in 2007 (232 million hours).
13
Additionally, Airlines for America (A4A), in 2012, estimated the total system delay that
year as 92 million minutes (Airlines for America, 2013). This is estimated to have cost
airlines $7.2 billion in direct costs and billions of dollars more in passenger costs.
These various past studies across different years indicate that the brunt of flight delays
in the U.S. air transportation system is borne by passengers. According to the findings
of Bratu and Barnhart (2005), flight cancellations and missed connections contribute
significantly to domestic passenger delays. Hence, in this thesis, we analyze air travel
data across the 2006 - 2010 time period to understand the factors that cause flight
cancellations and missed connections.
1.1 Data Sources
In order to conduct our analysis, we use data on flights and passengers for each year
across the 2006 - 2010 time period. For each year, we obtain publicly available flight ontime performance data for airlines that serve at least 1% of the U.S. domestic
passengers. Our data source is the Airline Service Quality Performance (ASQP)
database (Bureau of Transportation Statistics, 2006, 2007, 2008, 2009, 2010). In order to
obtain passenger data at the itinerary level (whereby a passenger itinerary refers to a
scheduled non-stop or one-stop, one-way passenger trip), we use estimated passenger
itinerary flows that were developed by Barnhart, Fearing, and Vaze (2013). These are
used because publicly available aggregate data on passenger trips is insufficient. The
publicly available data reports flows based on the origin, connection (if any), and
destination airports of the passengers on a quarterly basis, or passenger flows based on
origin-destination airports for each non-stop segment on a monthly basis. Disaggregate
data on an itinerary level is not available publicly. Such data is particularly vital in our
analysis of misconnections.
In order to disaggregate passenger demand data into individual itineraries, Barnhart,
Fearing, and Vaze (2013) use two datasets that are maintained by the Bureau of
Transportation Statistics (BTS) - Schedule T-100, which includes aggregated passenger
travel data for flights within the continental U.S. that were operated by U.S. carriers,
and the Airline Origin and Destination Survey (DB1B) database, which provides a 10%
sample of domestic passenger trips for reporting carriers including those contained in
ASQP (Bureau of Transportation Statistics, 2006, 2007, 2008, 2009, 2010). The T-100
dataset contains passenger demand data aggregated by month for each carrier-segment.
Here, a carrier-segmentis defined as the combination of a carrier, origin and destination,
where the carrier provides non-stop service between the origin and destination. The
DB1B dataset, however, contains passenger demand data that is aggregated by quarter
(every three months) for each carrier-route.Here, a carrier-routeis defined as a sequence
14
of either one or two carrier-segments representing either a non-stop or one-stop trip
respectively. For each year, the data is disaggregated by performing the following steps:
1. Based on each year's flight data from ASQP, plausible non-stop and one-stop, oneway itineraries are generated.
2. The carrier-route data obtained from DB1B for each of the five years is scaled
relative to the T-100 data to account for the 10% sampling and monthly variation.
3. A multinomial logit model is then estimated using proprietary booking data, and
used to allocate the individual carrier-routepassengers to matching itineraries.
Please refer to the Barnhart, Fearing and Vaze (2013) study for full details of the data
disaggregation process.
The analysis of cancellations in Chapter 2 uses the ASQP flight on-time performance
data, whereas the analysis of missed connections in Chapter 3 uses a combination of the
flight on-time performance data from ASQP and the passenger itinerary flows data
obtained as the output of the aforementioned disaggregation process.
Throughout this thesis, the airlines and airports are referenced by their International Air
Transportation Association (IATA) abbreviations, which we list in Appendix A.
1.2 Thesis Contributions
The work presented in this thesis provides an in-depth longitudinal analysis of the two
leading disruptions to passengers - flight cancellations and misconnections - across the
2006 - 2010 time period. We provide numerous insights that explain the variability
observed in cancellation rates and misconnection rates, and the impact of various
industry trends and airline scheduling practices on disruptions.
1.3 Thesis Outline
In Chapter 2, we focus on flight cancellations across airports and carriers, and the
trends observed across the years. We analyze cancellation rates at the top 50 busiest
airports and for the 20 major carriers in the U.S. across the 2006 - 2010 time period and
attempt to explain the variability observed at both the airport and carrier levels. We
identify flight frequency and load factors as two factors that significantly impact
cancellation rates and investigate the level of impact of these two factors across the
different carriers. Using regression models, we attempt to predict cancellation rates
using various explanatory variables, including the level of congestion at the origin and
15
destination airports, the type of carrier, the average flight frequency, the average load
factor, and whether or not the flights originate from the carriers' hubs.
In Chapter 3, we focus our analysis on missed connections across the top 50 busiest
airports and across the 20 major carriers in the U.S., and the observed trends in
misconnections across the years. We analyze the impact of arrival flight delay and
airline scheduling practices on the rate of misconnections, and use regression models to
attempt to predict misconnection rates using average flight delay, average connection
time, and the type of carrier as explanatory variables.
In Chapter 4, we discuss our main conclusions as well as provide some promising
directions for future research.
16
Chapter 2
Analysis of Flight Cancellations over the 2006 - 2010 Time Period
Flight cancellations are the second largest source of passenger delays in the National
Air Transportation System. For calendar year 2007, only 2.1% of passengers were
disrupted as a result of flight cancellations, and yet, these passengers accumulated
30.4% of the total passenger delays experienced for the year. Hence, it is important to
understand the factors that influence flight cancellations.
In this chapter, we attempt to identify these factors and determine their level of impact
on cancellation rates across the years from 2006 to 2010. The majority of our analysis of
flight cancellations is based on the flight on-time performance information provided in
the ASQP database. We use the term flight cancellation rate (or simply, cancellation
rate), defined as the ratio of the number of cancelled flights to the number of scheduled
flights, and we express this as a percentage.
2.1 Airports and Carriers
Flight cancellation rates vary dramatically across airports and carriers. These effects are,
however, strongly related because each airport has a different distribution of operations
(arrivals and departures) across carriers. In this sub-section, we demonstrate the
dependence of flight cancellation rates on airports and carriers and present this
dependence across the 2006 - 2010 time period.
For the analysis of cancellation rates across airports, we consider the top 50 busiest
airports in the United States based on the number of flight operations per day. This list
of airports remained the same over the 2006 - 2010 time period. These 50 airports
constituted 78.8% of the total operations in 2006, 77.9% in 2007, 78.1% in 2008, 78.5% in
2009, and 78.2% in 2010.
To confirm that the top 50 busiest airports create a suitable sample that is representative
of all the airports, we conduct a bootstrapping analysis. Bootstrapping is a statistical
technique that involves resampling a sample of data (with replacement) in order to
make an inference about the population. That is, bootstrapping treats inference of the
true probability distribution, P, of the population as being analogous to the inference of
the observed distribution of the resampled data, P.
17
For each year, we calculate the mean cancellation rate across all the airports, and define
this as the mean of the population, Mcamc. We then calculate the mean cancellation rate
for the top 50 busiest airports for each year and define this as the sample mean, Xcanc. In
our analysis, we resample from, N,, the sample of the top 50 busiest airports 1,000 times,
with replacement. We define each of the 1,000 samples obtained as: n, 1 , for] e
{1, 2, ..., 1,000}. For each of the 1,000 samples, we define the mean as: Xn, forj e
{1, 2, ..., 1,000}. The mean of the 1,000 samples, Xn,, is calculated as defined below in
Equation 1:
(Ej E[1,2,.i1,ooo
-s
kn,)
1,000
Now, we set four different confidence limits to define ranges around the value of Xn,
within which the true mean, Pcanc - should fall. We use 99%, 95%, 90%, and 85%
confidence limits, with 85% achieving the tightest bound around -n, and 99% providing
the loosest bound. Our results are provided in Table 2.1.1 below:
Year
2006
2007
Icanc
1.71%
2.16%
Xcanc
1.63%
2.06%
85%
Confidence
Interval
1.26%
90%
Confidence
Interval
1.23%
95%
Confidence
Interval
1.15%
99%
Confidence
Interval
1.05%
1.99%
2.04%
2.22%
2.41%
-
1.59%
2.55%
2008
1.96%
1.88%
1.39%
1.31%
-
1.46%
1.76%
1.68%
1.43%
-
1.04%
1.01%
-
-
2.07%
1.32%
2.11%
-
1.34%
-
0.95%
-
1.24%
2.17%
-
1.26%
-
2.71%
-
1.70%
-
1.34%
3.05%
2.58%
1.67%
1.33%
1.47%
2.77%
2.42%
1.58%
2010
-
2.65%
2.34%
2009
1.56%
0.88%
-
1.90%
-
1.14%
-
2.4%
Table 2.1.1: Range of values within which the true mean should fall
According to the results in Table 2.1.1 above, for all the years, Pcanc falls within each of
the confident limits. This suggests that the sample of the top 50 busiest airports is a
good estimation of the entire airport population and, therefore, the results we obtain
from our analysis at the airport level can be used to make conclusions about all the
airports in the U.S.
18
Next, we present the results of the analysis of cancellations at the airport level across the
years over the 2006 - 2010 time period.
In Figure 2.1.1 below, we present the average cancellation rate for each of the top 50
busiest airports, in decreasing order for the 2006 calendar year.
% of Cancelled Flights in 2006
4.00%
3.50%
3.00%
2.50%
-Top
0
lim-t
2.00%
1.50%
50
Average
Cancellatio
n Rate
1.00%
0.50%
UJ.1Uu/o
I I
I
I
-
1
I
I
1
1111
11 11
f
Airport
Figure 2.1.1: Cancellation rates for the top 50 busiest airports in 2006
For 2006, the overall cancellation rate among all airports was 1.71% while the
cancellation rate among the top 50 was 1.63%. 17 out of the top 50 airports had a
cancellation rate higher than the overall average as well as higher than the average
across the top 50. After the first two airports in terms of cancellation rates (LGA and
ORD), there was a significant drop-off in cancellation rates, after which no other airport
had an overall cancellation rate higher than 3.00%. The overall cancellation rate of LGA
and ORD was 3.52%, which is 2.5 times as high as the overall cancellation rate of the
remaining 48 airports (1.44%). LGA (3.70%) and ORD (3.26%) were the only two
airports whose cancellation rates were more than twice the overall average. These two
airports contributed to only 7.02% of all the flights but contributed to 14.46% of all the
cancellations recorded in the year, which corresponds to 8.90% of all the flights at the
top 50 airports and 19.25% of the cancellations at the top 50 airports.
19
In terms of the number of cancellations observed in the year, the top three airports were
ATL, ORD, and DFW. These three airports were also the top three in terms of number
of departures observed and hence the busiest as per our definition of busy. The airports
contributed to 15.16% of system-wide departures, and 20.54% of cancellations which
corresponds to 19.24% of the total departures across the top 50 busiest airports, and
27.34% of the total cancellations made across the top 50 busiest airports. ATL was the
busiest domestic airport, accounting for 5.71% of total system-wide departures, but only
5.43% of the total system-wide cancellations; of these top three airports, ATL was the
only airport whose overall cancellation rate (1.62%) was below the overall average, as
well as below the average across the top 50 airports. ORD was the second busiest
domestic airport, accounting for 5.23% of the total system-wide departures but had the
largest number of cancellations, accounting for 10.59% of the total system-wide
cancellations. DFW accounted for 4.22% of the total system-wide departures and 4.51%
of the total system-wide cancellations.
Figure 2.1.2 below provides the cancellation rates for each of the top 50 busiest airports
in decreasing order for the 2007 calendar year.
% of Cancelled Flights in 2007
6.00%
5.00%
4.00%
C
3.00%
(U
-
111
!I
% 2.00%
Top 50
Average
Cancellati
on Rate
1.00%
L.
U500%
I,
Airport
Figure 2.1.2: Cancellation rates for the top 50 busiest airports in 2007
For 2007, the overall cancellation rate among all airports was 2.16% while the
cancellation rate among the top 50 was 2.06%. This was a 26.32% increase in the overall
20
cancellation rate from 2006. 15 out of the top 50 airports had a cancellation rate higher
than the overall average and 16 of the top 50 had a cancellation rate higher than the
average across the top 50. After the first eight airports in terms of cancellation rates
(LGA, ORD, EWR, DCA, BOS, JFK, IAD, and DFW), there was a significant decrease in
cancellation rates, after which no other airport had a cancellation rate that was higher
than even 2.50%. The overall cancellation rate of these eight airports was 3.82%, which
was more than 2.5 times as high as the overall cancellation rate of the remaining 42
airports (1.50%). These eight airports contributed to only 18.59% of all the flights but
contributed to 32.97% of all the cancellations recorded in the year, which corresponds to
23.86% of the total flights among the top 50 airports and 44.35% of the total
cancellations among the top 50 airports.
Of note, JFK recorded one of the highest increases in cancellation rate among the top 50
airports from 2006 (1.75%) to 2007 (3.24%). In addition, the airport's rank in terms of the
highest cancellation rate changed from the 17th position in 2006 to the 6th position in
2007. This can be attributed to the infamous Valentine's Day snow storm that occurred
on February 1 4 th of that year; the storm produced severe thunderstorms that heavily
affected the eastern half of Northern America, and in particular disrupted flight
operations at JFK. To investigate this further, we analyze cancellation rates at JFK
month-wise as shown Table 2.1.2 below.
Month
Cancellation
Rate at JFK
January
1.32%
February
10.66%
March
4.76%
April
3.09%
May
0.95%
June
4.53%
July
3.95%
August
2.79%
September
0.96%
October
1.97%
November
0.71%
December
3.62%
Table 2.1.2: Month-by-month cancellation rates at JFK in 2007
21
As expected, JFK recorded the highest monthly cancellation rate in February at 10.66%.
If we eliminate February's operations in the calculation of the overall average
cancellation rate for the year, we find that JFK's cancellation rate drops from 3.24% to
2.63%, and moves the airport's rank from 6 th position in terms of highest cancellation
rate to 11th position, among the top 50 busiest airports.
Similar to 2006, the top three airports in terms of the number of cancellations observed
in the year were ATL, ORD, and DFW. These three airports were also the top three in
terms of number of departures observed that year and hence the busiest. In 2007, the
ranking among these three airports was similar to that in 2006, and the percentage of
operations at each of the three airports remained fairly similar. ATL's cancellation rate
(1.62%) was well below the overall average cancellation rate (2.16%), and it was the only
airport among the three whose cancellation rate was below the overall cancellation rate
as well as below the overall cancellation rate at the top 50 airports. ORD was the second
busiest domestic airport that year and also the domestic airport with the highest
number of flight cancellations.
Figure 2.1.3 below provides the cancellation rate for each of the top 50 busiest airports
in decreasing order for the 2008 calendar year.
% of Cancelled Flights in 2008
6.00%
5.00%
4.00%
3.00%
-Top
U
Average
Cancellation
Rate
m 2.00%
1.00%
50
-p
0.00%
Airport
Igp
Figure 2.1.3: Cancellation rates for the top 50 busiest airports in 2008
22
For 2008, the overall cancellation rate among all airports was 1.96% while the
cancellation rate among the top 50 was 1.88%. 15 out of the top 50 airports had a
cancellation rate higher than the overall average and 16 of the top 50 had a cancellation
rate higher than the average across the top 50. After the first two airports in terms of
cancellation rates (LGA and ORD), there was a significant decrease in cancellation rates,
whereby no other airport had a cancellation rate higher than even 3.50%. Interestingly,
this is a similar observation as that for 2006. Note that 2007 was the peak year in terms
of congestion and cancellations, after which they both decreased and presumably, as a
result, the general patterns of 2006 in terms of the cross airport cancellation rates
returned in 2008. The overall cancellation rate of LGA and ORD was 4.43%, which was
more than 2.7 times as high as the cancellation rate of the remaining 48 airports (1.64%).
These two airports contributed to only 6.07% of all the flights but contributed to 15.14%
of all the cancellations recorded in the year, which corresponds to 8.60% of the total
flights among the top 50 airports and 20.28% of the total cancellations made by the top
50 airports.
In terms of the number of cancellations observed in the year, the top three airports were
ATL, ORD, and DFW, similar to the previous two years. These three airports were also
the top three in terms of number of departures observed that year and hence the
busiest. Again, similar to 2006 and 2007, ATL was the busiest domestic airport and the
only airport among the top three whose cancellation rate (1.41%) was below the overall
average (1.96%) and below the overall cancellation across the top 50 airports (1.88%).
ORD was the second busiest domestic airport, and DFW ranked 3rd in terms of busiest
airports.
In Figure 2.1.4 below, we present the average cancellation rate for each of the top 50
busiest airports in decreasing order for the 2009 calendar year.
23
% of Cancelled Flights in 2009
4.50%
4.00%
3.50%
r 3.00%
o 2.50%
2.00%
1.50%
1.00%
-Top
50
Average
HIM-
Cancellatio
n Rate
0.50%
TI
U.UUo70
I
I
I
Airport
Figure 2.1.4: Cancellation rates for the top 50 busiest airports in 2009
For 2009, the overall cancellation rate among all airports was 1.39% while the
cancellation rate among the top 50 was 1.31%. 13 out of the top 50 airports had a
cancellation rate higher than the overall average and 15 of the top 50 had a cancellation
rate higher than the average across the top 50. After the first airport in terms of
cancellation rates (LGA), there was a significant decrease in cancellation rates. The
overall cancellation rate of LGA was 3.98%, which is more than three times as high as
the cancellation rate of the remaining 49 airports (1.25%). Interestingly, LGA maintained
its position as the airport with the highest cancellation rate while ORD, which had been
the airport with the second-highest cancellation rate since 2006, moved down the list to
the 6t1 position. LGA contributed to 1.56% of all the flights and contributed to 4.48% of
all the cancellations recorded in the year, which corresponds to 2.00% of the total flights
across the top 50 airports and 6.09% of the total cancellations across the top 50 airports.
In 2009, the airport that had the second-highest cancellation rate was BOS and not ORD
as it had been over the past three years. BOS moved from the 5t position in 2008 in
terms of highest cancellation rate, to 2nd position in 2009; the airport's cancellation rate,
however, decreased from 3.10% in 2008 to 2.61% in 2009. This change in rank was only
as a consequence of the significant decrease in ORD's cancellation rate.
In terms of the number of cancellations observed in the year, the top three airports were
ATL, ORD, and DFW, similar to the previous years. These three airports were also the
24
top three in terms of number of departures observed and hence the busiest.
The
ranking in terms of number of operations among these three airports remained the
same as in the previous years. Similar to the previous year, ATL was the busiest airport,
and also the only one among the top three busiest airports whose cancellation rate
(1.27%) was below the overall average (1.39%) as well as below the average at the top 50
busiest airports (1.31%). ORD was the second busiest airport in terms of number of
flights, accounting for 4.88% of all departures and the highest number of flight
cancellations (7.64% of total cancellations). Yet, this represented a decrease in the
contribution made by ORD in terms of the total number of flight cancellations - from
10.95% of all cancellations in 2008 to 7.64% of all cancellations in 2009. Furthermore,
ORD's rank among the top 50 airports in terms of cancellation rates improved from 2nd
position in 2006 - 2008, to
6 th
position in 2009. This is attributed to the reduction in
congestion at the airport as a result of a new runway, Runway 9L/27R', which was
opened in November, 2008. DFW was the third busiest domestic airport.
Figure 2.1.5 below presents the average cancellation rates for each of the top 50 busiest
airports in decreasing order for the 2010 calendar year.
% of Cancelled Flights in 2010
4.50%
4.00%
3.50%
C
li
3.00%
Top 50 Average
Cancellation Rate
o 2.50%
2.00%
i 1.50%
1.00%
----
------ -----
0.50%
Airport
Figure 2.1.5: Cancellation rates for the top 50 busiest airports in 2010
Runway 9L/27R was part of a modernization program at O'Hare that was aimed at reducing delay and
enhancing capacity at the airport (O'Hare Modernization Program). The runway is located in the north of
the airport and allows accommodation of three arrival streams, and allows great delay reduction during
bad weather (airport-technology.com, 2013)
25
For 2010, the overall cancellation rate among all airports was 1.76% while the
cancellation rate among the top 50 was 1.68%. 17 out of the top 50 airports had a
cancellation rate higher than the overall average and 19 of the top 50 had a cancellation
rate higher than the average across the top 50. After the first two airports in terms of
cancellation rates (LGA and DCA), there was a significant decrease in cancellation rates
(whereby no other airport had a cancellation rate higher than even 3.50%), and another
significant decrease after the first four airports (LGA, DCA, JFK, and EWR), whereby no
other airport had a cancellation rate higher than 3.00%. The average cancellation rate for
DCA increased from 2.42% in 2009 to 4.13% in 2010, which was more than a 70%
increase in average cancellation rate. On further investigation of the distribution of
carriers that operate at DCA, we find that DL's share of total operations at the airport
increased from 7.8% in 2009 to 15.1% in 2010, making it the carrier with the secondhighest share of total operations at the airport. This was a consequence of DL's merger
with NW which was finalized in 2010. Furthermore, DL was the carrier that had the
highest increase in average cancellation rate from 2009 to 2010 (this will be further
discussed later in this section). Therefore, the large increase in DCA's cancellation rate
is a consequence of DL's increase in average cancellation rate, which may be attributed
to the operational changes brought about by the merger. The overall cancellation rate
for LGA and DCA was 4.19%, which is more than 2.6 times as high as the overall
cancellation rate at the remaining 48 airports (1.59%). The overall cancellation rate of the
first four airports in terms of cancellation rate was 3.70%, which is 2.5 times as high as
the overall cancellation rate of the remaining 46 airports (1.51%). These four airports
contributed to only 6.32% of all the flights but contributed to 13.33% of all the
cancellations recorded in the year, which corresponds to 8.12% of all the flights across
the top 50 airports and 17.87% of the total cancellations across the top 50 airports.
In terms of the number of cancellations observed in the year, the top three airports were
ATL, ORD, and DFW, similar to the previous years. These three airports were also the
top three in terms of number of departures observed and hence the busiest. Similar to
the previous years, ATL was the busiest domestic airport, accounting for 6.43% of total
flights and 7.24% of the total flight cancellations. In 2010, however, ATL was the airport
with the highest number of cancellations among these three airports - higher than ORD
which accounted for 6.87% of total cancellations. In addition, ATL's overall rank among
the 50 busiest airports in terms of cancellation rate dropped from 17h position in 2009 to
12t* position in 2010. ORD was the second busiest domestic airport, accounting for
26
4.86% of the total flight departures, and 6.87% of the total flight cancellations. DFW was
the third busiest domestic airport and was the only airport among the three whose
cancellation rate (1.68%) was below the overall average cancellation rate (1.76%).
Over the entire 2006 - 2010 time period, LGA remained at the top of the list as the
airport with the highest overall cancellation rate among the top 50 busiest airports,
reaching its highest cancellation rate in 2007 (5.21%). The top five airports in terms of
cancellation rates remained the same from 2006 - 2008. These airports were LGA, ORD,
EWR, DCA, and BOS. In 2009 and 2010, ORD dropped out of the top five and was
replaced by JFK. This was as a result of the addition of a new runway at ORD, as
mentioned earlier. In terms of the busiest airports (those with the highest number of
departures), ATL, ORD, and DFW remained as the top three airports and their
contribution to the total flights and cancellations remained approximately the same
over the entire 2006 - 2010 time period.
To conduct carrier-specific analysis, we first classify all the carriers that have less than
80% of their operations in the continental U.S. as non-continental carriers. The three
carriers that fall under this category are: Aloha Airlines (AQ), Hawaiian Airlines (HA),
and Alaskan Airlines (AS). We further categorize the remaining 17 continental carriers
into three groups: legacy network carriers, low-cost carriers, and regional carriers.American
Airlines (AA), Continental Airlines (CO), Delta Airlines (DL), Northwest Airlines (NW),
United Airlines (UA), and US Airways (US) are categorized as legacy network carriers (or
simply, legacy carriers); JetBlue Airways (B6), Frontier Airlines (F9), AirTran Airways
(FL), and Southwest Airlines (WN) are classified as low-cost carriers; and Pinnacle
Airlines (9E), Atlantic Southeast Airlines (EV), American Eagle Airlines (MQ), Comair
(OH), SkyWest Airlines (00), ExpressJet Airlines (XE), and Mesa Airlines (YV) are
classified as regional carriers. For our analysis, we categorize a passenger, who is
scheduled to travel on a one-stop itinerary that includes flights operated by two
different carriers, based on the carrier operating the first flight of the itinerary.
We conduct a longitudinal study comparing cancellation rates across the four categories
of carriers across the 2006 - 2010 time period. Figure 2.1.6 below is a chart that provides
the average cancellation rate for each group for each year.
27
Cancellation Rates by Carrier Group
3.50%
3.00%
.
2.50%
&0000,-
2.00%
15%
-+-Legacy Carriers
Low-Cost Carriers
1.00%
-*--Regional Carriers
0.50%
-+-Non-Continental
Carriers
0.00%
2006
2007
2009
2008
2010
Year
Legacy Carriers
Low-Cost Carriers
Regional Carriers
Non-Continental Carriers
2007
1.99%
0.97%
3.20%
1.21%
2006
1.38%
0.79%
2.74%
1.27%
2008
1.84%
1.03%
2.82%
1.27%
2009
1.24%
0.82%
2.02%
0.63%
2010
1.65%
1.13%
2.42%
0.42%
Figure 2.1.6: Cancellation rates for each carrier group across the 2006 - 2010 time
period
Over the entire 2006 - 2010 time period, the regional carriers' group had the highest
cancellation rates of the four groups, and the legacy carriers had the second-highest
cancellation rates. From 2006 to 2008, the low-cost carriers had the lowest cancellation
rates among the four carrier groups and the non-continental carriers had the secondlowest cancellation rates; in 2009 and 2010, the non-continental carriers' group had the
lowest cancellation rates among the four carrier groups while the low-cost carriers had
the second-lowest cancellation rates. Among the continental carriers, both the legacy
carriers and the regional carriers had the highest cancellation rate in 2007 (1.99% for the
legacy carriers and 3.20% for the regional carriers) but the low-cost carriers had the
highest cancellation rate in 2010 (1.13%).
Next, we present figures in which we plot the cancellation rate for each airline arranged
in decreasing order, by year. In the plots, the regional carriers are highlighted in blue,
28
the legacy network carriers in green, the low-cost carriers in orange, and the noncontinental carriers in grey. The first plot below in Figure 2.1.7 is for data gathered for
2006.
Cancellation Rates by Carrier - 20061
4.00%
3.50%
3.00%
C 2.50%
--
2.00%
1.50%
t
Overall Average
Cancellation Rate
1.00%
0.50%
A
/
Carrier
Figure 2.1.7: Cancellation rates for each carrier in 2006
According to Figure 2.1.7 above, the first six carriers in terms of the highest cancellation
rates are all regional carriers. All the regional carriers have cancellation rates that are
above the overall average cancellation rate, while all the low-cost and non-continental
carriers have cancellation rates that are below the overage cancellation rate for that year.
Among the legacy carriers, UA (2.05%) had the highest cancellation rate and was the
only carrier whose cancellation rate was above the overall average cancellation rate. AA
(1.57%) had the second-highest cancellation rate among the legacy carriers. Among the
regional carriers, MQ (3.63%) had the highest cancellation rate that year.
Figure 2.1.8 below represents the plot for the cancellation rates for each airline in
decreasing order, in 2007.
29
Cancellation Rates by Carrier - 2007
5.00%
4
4.00%
o 3.00%
2.00%
UM 1.00%
A AAL
.
mum
m]J
W< W
Overall Average
1
o
nV
_ -
. ..
Cancellation Rate
zC
Carrier
Figure 2.1.8: Cancellation rates for each carrier in 2007
According to Figure 2.1.8 above, the first five carriers in terms of highest cancellation
rate were all regional carriers. All the regional carriers had cancellation rates that were
higher than the overall average cancellation rate that year. Among the legacy carriers,
AA (2.83%) and UA (2.43%) had the highest cancellation rates and were the only legacy
carriers with cancellation rates that were above the overall average. AA observed an
increase in cancellation rates from 2006 (1.57%) to 2007 (2.83%); this was an 80.25%
increase in cancellation rate and was the highest increase among the legacy carriers. The
carrier also moved from 8th position in 2006 to 6t* position in 2007 in terms of highest
cancellation rates among the carriers. DL was the only legacy carrier that observed a
decrease in cancellation rate from 1.52% in 2006 to 1.37% in 2007. DL's ranking changed
from 9t* position in 2006 to 14t" position in terms of highest cancellation rate among the
carriers.
The low-cost carriers and the non-continental carriers all had cancellation rates that
were below the overall average cancellation rate. B6 observed the highest increase in
cancellation rate among the low-cost carriers as well as among all other carriers, from
0.42% in 2006 to 1.94% in 2007. Its ranking in terms of highest cancellation rates across
all the carriers changed from 18t" position in 2006 (it was the carrier with the lowest
cancellation rate across all carriers in 2006) to 10t" position in 2007. The reason for this is
that B6 operates a hub out of JFK and was therefore adversely affected by the
Valentine's Day Storm in February of 2007. To confirm this, we calculate the carrier's
30
cancellation rates on a month-by-month basis and provide the results below in Table
2.1.3.
Month
January
February
March
April
May
June
July
August
September
October
November
December
Table 2.1.3: Month-by-month
B6's
Cancellation
Rate
0.28%
9.17%
3.09%
1.76%
0.32%
2.49%
1.57%
0.94%
0.71%
0.69%
0.24%
2.48%
cancellation rates for B6 in 2007
From the above results, it is evident that February was the worst month in terms of
average cancellation rate for B6. When we exclude the month of February in calculating
B6's average cancellation rate for the year, we obtain an average of 1.34%, which would
move the carrier's rank from 10th position to 14 th position among the 20 carriers.
Figure 2.1.9 below presents the cancellation rates for all the carriers in decreasing order
in 2008.
31
Cancellation Rates by Carrier - 2008
4.00%
3.50%
3.00%
0
-
2.50%
2.00%
1.50%
% 1.00%
0.50%
Overall Average
Cancellation Rate
n nnO%
> M<
2>_<MX
< 0> k
0W
U10 z<
0D<
_j
c
M '-z L <
Carrier
Figure 2.1.9: Cancellation rates for each carrier in 2008
According to Figure 2.1.9 above, the first three carriers in terms of highest cancellation
rates were regional carriers. Six out of the seven regional carriers had cancellation rates
that were higher than the overall average cancellation rate in 2008 (only EV had a
cancellation rate that was lower than the overall average cancellation rate).EV's
cancellation rate fell from 3.12% in 2007 to 1.79% in 2008 (a 74% decrease in average
cancellation rate).
Similar to 2007, AA (2.88%) and UA (2.34%) were the only two
legacy carriers whose cancellation rates that year were higher than the overall average
cancellation rate. Similar to the previous two years, all the low-cost carriers and the
non-continental carriers had cancellation rates that were below the overall average
cancellation rate that year.
Figure 2.1.10 below presents the cancellation rates for all the carriers in decreasing order
in 2009.
32
Cancellation Rates by Carrier- 2009
4.00%
3.50%
3.00%
a)0
(U
2.50%
2.00%
-Overall
Rate
1.50%
1.00%
Average Cancellation
0.50%
-
0.00%
-
M11
.
)
<
--
M
Carrier
Figure 2.1.10: Cancellation rates for each carrier in 2009
In 2009, the first six carriers with the highest cancellation were all regional carriers. Six
of the seven regional carriers had cancellation rates lower than the overall cancellation
rate; 00 was the only regional carrier whose cancellation rate was lower than the
overall average cancellation rate.
Among the legacy carriers, UA (1.69%) and AA (1.68%) were the only carriers whose
cancellation rates were above the overall average cancellation rate. Similar to previous
years, all the low-cost carriers and the non-continental carriers had cancellation rates
that were below the overall average cancellation rate.
Figure 2.1.11 below represents the plot for the cancellation rates for each airline in
decreasing order, in 2010.
33
Cancellation Rates by Carrier - 2010
4.50%
4.00%
4
3.50%
3.00%
o 2.50%
=m 2.00%
"-OverallAverage Cancellation
Rate
a 1.50%
U 1.00%
0.50%
U.u
U U za
1
1
1
1
1
1
1
1
1
1i
1
1
1
Carrier
Figure 2.1.11: Cancellation rates for each carrier in 2010
In 2010, we observe some changes from previous years in regards to the ranking of the
carriers. Similar to the previous years, the first five carriers in terms of highest
cancellation rates observed that year were all regional carriers. In addition, all the
regional carriers had cancellation rates that were above the overall average cancellation
rate. Interestingly, OH and 9E were the top two carriers in terms of cancellation rates,
ousting MQ from either of these positions; both OH and 9E are regional subsidiaries for
DL which had an increase in cancellation rate. We explore this phenomenon further in
Section 2.2. Among the legacy carriers, DL had the highest cancellation rate and was the
only legacy carrier whose cancellation rate was higher than the overall average
cancellation rate; DL observed the highest increase in cancellation rate, from 1.12% in
2009 to 2.03% in 2010. This is explored further in the succeeding sections. Another
change is observed among the low-cost carriers - B6 had the highest cancellation rate
among the low-cost carriers, and was the only carrier within the group whose
cancellation rate was higher than the overall average cancellation rate.
There exists interdependence between carrier-specific and airport-specific factors, and it
can be difficult to separate out carrier performance from the impacts of airports. In
order to give an illustration of this interdependence, we explore the distribution of
flight operations at the two airports which had the worst cancellation rates for most of
the 2006 - 2010 time period - LGA and ORD. In Figure 2.1.12 below, we show the
34
average distribution of flight departures by the major carriers at these two airports
across the 2006 - 2010 time period.
Average Distribution of Flight
Departures at LGA and ORD
Others,
16.59%
MO, 22.27%
DL, 5.1000
YV, 5.82%
UA, 20.54%
00, 10.11%
AA, 19.57%
Figure 2.1.12: Average distribution of flight departures at LGA and ORD over the
2006 - 2010 time period
According to Figure above, MQ, UA, and AA were the top carriers at LGA and ORD
across the 2006 - 2010 time period. Of the total flights that departed out of either LGA
or ORD, an average of 22.27% were operated by MQ, 20.54% by UA, and 19.57% by AA.
In addition, during the 2006 - 2010 time period, an average of 19.12% of the flights
operated by either MQ, UA, or AA departed from either LGA or ORD. Furthermore,
these three carriers also had very high cancellation rates, with MQ recording the highest
cancellation rate across all the carriers from 2006 until 2008, and UA and AA being the
legacy carriers with the highest cancellation rates from 2006 until 2009. MQ, UA, and
AA had an average cancellation rate of 2.50% across the five years.
We explore the interdependence between the carrier-specific and airport-specific factors
further in detail in section 2.4.
35
2.2 Flight Frequency and Load Factors
Flight frequency and load factors significantly affect an airline's decision as to whether
or not it should cancel a flight (Rupp and Holmes, 2005; Tien, Churchill and Ball, 2009).
In this section, we use correlation to analyze how these two factors impact the
cancellation decision process across different carriers.
For our analysis, we compute average daily flight frequencies, average flight
cancellation rates, and average load factors for each airline's carrier-segments (as
previously defined in sub-section 1.1). We focus our analysis on carrier-segments that
have an average daily frequency of at least one flight, and that originate from the top 50
airports. Using these carrier-segments we conduct a longitudinal analysis across the
2006 - 2010 time period.
We calculate the correlation between flight frequency and cancellation rates for the four
carrier groups across the time period and provide the results in Table 2.2.1 below.
36
Legacy
Carriers
Low-cost
Carriers
Regional
Carriers
Noncontinental
Carriers
31.10%
Correlation
31.00%
51.30%
8.10%
in
Coefficient
2006
P-value
0.00
0.00
0.00
0.00
Correlation
34.80%
36.40%
7.80%
19.80%
in
Coefficient
2007
P-value
0.00
0.00
0.00
0.02
Correlation
31.50%
44.40%
2.10%
23.20%
in
Coefficient
2008
P-value
0.00
0.00
0.44
0.01
Correlation
30.80%
46.30%
-1.00%
13.80%
in
Coefficient
2009
P-value
0.00
0.00
0.72
0.05
Correlation
22.50%
27.90%
-1.50%
-6.90%
in
Coefficient
2010
P-value
0.00
0.00
0.6
0.51
Table 2.2.1: Correlation between average flight frequency and flight cancellation
rates across the four carrier groups over the 2006 - 2010 time period
According to our results above, there was a high and positive correlation between
average flight frequency and cancellation rates for both the legacy carrier group and the
low-cost carrier group. The correlation coefficients for these two carrier groups were
positive and statistically significant across the entire period, which means that average
flight frequency was a significant factor in these carriers' decision regarding whether or
not to cancel flights. The positive sign of the correlation coefficients implies that airlines
are more likely to cancel flights that are on high-frequency legs as opposed to those that
are on low-frequency legs. This makes sense since higher frequency implies the
availability of more flights to re-accommodate passengers who are scheduled to fly on
the cancelled flights. The correlation coefficient estimates obtained for the noncontinental carriers were positive and statistically significant at 5% level across the
period, except in 2010, whereby the correlation coefficient was negative and was not
37
statistically significant. A negative sign for the correlation coefficient is counter-intuitive
because it implies that the associated carrier prefers to cancel flights on legs that have
less frequency. The correlation coefficients for the regional carriers were the weakest
among all the carriers during the time period, and in 2009 and 2010 the coefficient
estimates had a negative value and were not statistically significant. These results
suggest that for regional carriers, the decision as regards whether or not to cancel flights
were not as heavily influenced by the flight frequency, as in the case for the other
continental carriers. Since the regional carriers operated a significant share of the total
flights in the continental U.S. (an average of more than 33% of the flights operated in the
continental U.S. within the time period were operated by regional carriers), we
investigate this anomaly more thoroughly.
2.2.1 Regional Carriers Analysis:
In the U.S., regional carriers operate short- and medium-haul scheduled airline service,
often with the purpose of connecting smaller communities with large cities and
providing air service to under-served communities (Forbes and Lederman, 2006).
Almost all regional airlines operate under codeshare agreements with one or more
legacy carriers; in 2003, 99% of regional carrier passenger traveled on flights that were
code-shared with a legacy carrier (Forbes and Lederman, 2006).
For our analysis, we use data from seven major regional carriers, and give a brief
operational history for each below.
1. Pinnacle Airlines (9E): Now known as Endeavor Air operates as Delta
Connection for Delta Air since May 1V, 2013. It has hubs in MSP, DTW, LGA,
JFK, ATL, and MEM. In 2006-2007, the airline was a connection for NW. After
April 30, 2007, 9E flew for Delta Connection. On July 1, 2010, Memphis-based
Pinnacle bought Delta Airlines' wholly owned subsidiary, Mesaba Aviation Inc.
2. Atlantic Southeast Airlines (EV): This airline was based in College Park, Georgia
and flew to 144 destinations as a Delta Connection. As of February 2010, EV
commenced service as a United Express carrier. In Nov. 2011, EV and ExpressJet
received a single operating certificate and in December of the same year, all
flights were branded as ExpressJet.
3. American Eagle Airlines (MQ): The airline's hubs are: DFW, JFK, MIA, and ORD.
The airline is now known as Envoy Inc. American Eagle, however, is still the
38
name of a marketing brand used by Envoy, as well as Republic Airlines based in
Indianapolis, ExpressJet Airlines based in Atlanta, and SkyWest Airlines based in
Salt Lake City, in the operation of passenger air services as the regional affiliates
for American Airlines; these airlines serve as affiliates for other airlines as well,
but Envoy is a wholly owned subsidiary of American Airlines. It's considered to
be the world's largest regional airline system. Until April 11, 2012, Envoy had a
code share agreement with Delta on California routes.
4. Comair (OH): This airline was a wholly owned subsidiary of Delta Air Lines and
ceased operations on September 29t", 2012. It was headquartered in CVG. In late
2006, the airline opened an additional crew base and hub in JFK. During late
2006, the airline had the lowest percentage of on time flights of all major U.S.
carriers as a result of this operation change.
5. SkyWest Airlines (00): The airline is headquartered in St. George, Utah. It flies
as SkyWest Airlines in a partnership with Alaska Airlines, as United Express on
behalf of United Airlines, as US Airways Express on behalf of US Airways, as
Delta Connection on behalf of Delta Air Lines, and as American Eagle on behalf
of American Airlines. 00 operates on behalf of American Eagle at LAX and ORD
; on behalf of SkyWest/Alaska Airlines at PDX and SEA; on behalf of Delta
Connection at CVG, DTW, LAX, MEM, MSP, SLC, and SEA; and on behalf of
United Express at ORD, CLE, DEN, IAD, HOU, LAX, PDX, and SFO; and on
behalf of US Airways Express at PHX.
6. ExpressJet Airlines (XE): EV and XE began to report jointly on Jan 2012.
ExpressJet operated using the Continental Express brand name and served as
Continental's subsidiary out of IAH, EWR, and CLE. It received a single
operating certificate with EV and currently operates as American Eagle, Delta
Connection, and United Express.
7. Mesa Airlines (YV): The airline is based in Phoenix, Arizona. It operated as go! at
HNL and KOA (until April 1, 2014); as US Airways Express at CLT and PHX;
and as United Express: ORD and IAD.
In this analysis, we consider the main legacy carriers served by the regionals during the
2006 - 2010 time period. Table 2.2.2 below summarizes the regional carriers, the legacy
carriers they served, and the hubs at which they served the respective legacy carriers:
39
Regional Carrier
Legacy Carrier(s) Served
Hub Airports
Atlantic Southeast Airlines Delta Airlines (DL)
MEM, ATL, DTW
(EV)
United Airlines (UA) in ORD, IAD
2010
SkyWest Airlines (00)
Delta Airlines (DL)
CVG, DTW, MEM, MSP,
SLC, SEA
United Airlines (UA)
ORD, CLE, DEN, IAD,
HOU, LAX, PDX, SFO
ExpressJet Airlines (XE)
Continental Airlines (CO)
CLE, EWR, IAH;
2009-2010: CLE, EWR, IAH,
ORD, IAD, MCI
Mesa Airlines (YV)
US Airways (US)
CLT, PHX
United Airlines (UA)
ORD, IAD
Pinnacle Airlines (9E)
Northwest Airlines (NW) MEM
2006 - 2007
Delta Airlines (DL) after DTW, MSP, JFK, MIA,
2007
ORD, ATL, MEM
American Eagle Airlines American Airlines (AA)
DFW, JFK, MIA, ORD
(MQ)
Comair (OH)
Delta Airlines (DL)
ATL, BOS, JFK, LGA, CVG
Table 2.2.2: Regional carriers, the legacy carriers they serve, and the hubs they
operate out of (the regional carriers in bold are/were wholly-owned by the respective
legacy carriers.)
Next, we present the number of carrier-segments operated by each regional carrier for
the respective legacy carrier out of the legacy carrier's hub airports, as a percentage of
the total number of carrier-segments operated by that regional carrier for that legacy
carrier. Here, as defined earlier, a carrier-segment is the combination of a carrier, origin,
and destination whereby the carrier provides non-stop flight access between the origin
and destination. Table 2.2.3 below summarizes these results.
40
2006
2007
2008
2009
2010
Avg. %
of
CarrierSegments
Regional
Carrier
EV
Legacy
Carrier(s)
Served
DL
UA
DL
UA
CO
US
UA
28.51%
29.72%
27.98%
31.71%
0.77%
15.05%
28.44%
49.17%
18.99%
21.51%
26.37%
33.73%
7.69%
19.26%
29.05%
50.28%
20.76%
20.76%
24.87%
30.33%
2.06%
14.51%
27.27%
42.15%
20.27%
19.20%
28.45%
12.15%
13.70%
25.44%
25.07%
XE
30.29%
31.38%
20.04%
19.67%
YV
18.95%
19.25%
26.97%
9E
DL
35.59%
NW
14.97%
42.20%
38.7%
38.44%
38.07%
38.03%
36.74%
MQ
AA
39.3%
38.33%
40.81%
40.80%
25.55%
37.0%
OH
DL
Table 2.2.3: Percentage of regional carriers' carrier-segments operated on behalf of
corresponding legacy carriers
00
12.39%
28.34%
49.63%
21.87%
15.51%
Because this analysis is based on the origin airport of the carrier-segments, the
percentages obtained above will be capped at 50% because departures make up 50% of
a carrier's total operations at any airport. Of all the carrier-segments served by the
regional carriers, less than 50% of each regional carrier's total carrier-segments
originated from hubs it served for its contracting legacy carrier(s), except for XE
whereby the number of carrier-segments operated out of CO's hubs by XE made up
about 50% of XE's total carrier-segments in 2006, 2009, and 2010.
Figure 2.2.1 below is a trend chart that serves as a visualization of Table 2.2.3.
41
60.00%
EVDL
-
50.00%
-EVUA
-OODL
40.00%
-OOUA
-XECO
3n AO/%
Yvus
YVUA
20.00% --
-9EDL
10.00%
MQAA
-OH
DL
-
0.00%
2006
2007
2008
2009
2010
Figure 2.2.1: Visual presentation of the percentage of regional carriers' carriersegments operated on behalf of corresponding legacy carriers
According to Figure 2.2.1 above, the percentage of carrier-segments served by the
regional carriers for the legacy carriers out of the respective hub airports remained
approximately constant for all regional carriers except for ExpressJet's operations for
Continental Airlines (represented in black) and Comair's operations for Delta Airlines
(represented in red). The percentage of carrier-segments served by XE for CO was lower
in 2007 and 2008 compared to the remaining 3 years. On further investigation, the total
number of carrier-segments operated by XE increased from 401 in 2006 to 657 in 2007,
and 650 in 2008, yet the number of carrier-segments originating from the hub airports
served for CO increased only slightly (from 199 in 2006 to 220 in 2007, and 227 in 2008).
Hence, the drop in the fraction of carrier-segments served by XE for CO was as a result
of an increase in the total number of unique origin-destination pairs operated by XE
rather than by a decrease in the number of carrier-segments operated by XE for CO. In
2007, the airline began service on behalf of DL as Delta Connection, an agreement which
was terminated later in 2008. This caused a temporary increase in total carrier-segments
served by XE.
Similarly for OH, there was a decrease in the percentage of carrier-segments served by
OH for DL in 2010. On further investigation, the total number of carrier-segments
42
served by OH from the hub airports of DL, in fact, increased from 2009 (204 carriersegments) to 2010 (273 carrier-segments). But the total number of unique carriersegments served by OH also increased, and at a slightly higher rate (from 500 in 2009 to
728 in 2010), leading to the reduction in percentage of carrier-segments served at the
hubs.. This was after the merger between DL and NW which was finalized in 2010.
Hence, the increase in unique carrier-segments can be attributed to the merger.
The next step in the analysis involves investigating the correlation between the
cancellation rates and frequency rates of the carrier-segments operated by the regional
carriers out of the respective legacy carriers' hub airports and comparing these with the
individual correlations of the respective regional carriers and the legacy carriers. The
reason for this analysis is to investigate whether the cancellation decisions taken by the
regional carriers at the hub airports at which they operate on behalf of the legacy
carriers are solely influenced by the respective legacy carriers.
We present correlations corresponding to each regional carrier and the corresponding
legacy carrier(s) it operated across the 2006 - 2010 time period.
43
9E and DL
40.00%
30.00%
20.00%
10.00%
0.00%
-+-DL
-1-9EDL
-10.00%
NN",
--
9EnonDL
-20.00%
-30.00%
2006
DL
9EDL
9EnonDL
2006
Correlation p-value
34.75%
0.00
2007
2007
Correlation p-value
26.28%
0.00
-7.20% 0.54
0.16%
0.58
2008
2009
2008
Correlation p-value
28.87%
0.00
-1.04%
0.63
-6.33%
0.40
2010
2009
Correlation p-value
19.74%
0.00
-14.33%
0.22
-19.14% 0.01
2010
Correlation p-value
22.16% 0.00
-5.23% 0.64
-5.68% 0.44
Figure 2.2.2: Correlation between average flight frequency and average flight
cancellation rates for DL, 9EDL, and 9EnonDL
Figure 2.2.2 above represents the trend in correlation between frequency rates and
cancellation rates of all carrier-segments operated by DL, those operated by 9E out of
hub airports for DL (here named 9EDL), and carrier-segments that do not serve DL
hubs (here named 9EnonDL). Of note, the correlation between flight frequency rates
and cancellation rates for 9EDL remained negative and very similar to the correlation
rate for 9EnonDL , over the 2006 - 2010 time period. However, this correlation is
statistically insignificant at 5% confidence level (except for (EnonDL in 2009), implying
that there isn't a strong linear dependence between flight frequencies and cancellation
rates for 9E flights, irrespective of whether or not they serve DL hubs. .
44
EV and DL
40.00%
30.00%
20.00%
oroor
10.00%
0.00%
-10.00%
-0-DL
Opp
-U-EVDL
-20.00%
~-h-- EVnonDL
-30.00%
2006
2006
Correlation p-value
34.75%
0.00
DL
EVDL
-0.3%
0.96
EVnonDi
6.66%
0.44
2007
2007
Correlation p-value
26.28% 0.00
-19.6% 0.06
-23.56%
0.02
2008
2008
Correlation p-value
28.87%
0.00
-19.5%
0.07
-14.10%
0.18
2009
2010
2009
Correlation p-value
19.74% 0.00
-6.5% 0.55
-16.99% 0.11
2010
Correlation p-value 1
22.16% 0.00
21.5% 0.02
15.93% 0.061
Figure 2.2.3: Correlation between average flight frequency and average flight
cancellation rates for DL, EVDL, and EVnonDL
Figure 2.2.3 above represents the trend in correlation between frequency rates and
cancellation rates of all carrier-segments operated by DL, those operated by EV out of
hub airports for DL (here named EVDL), and carrier-segments operated by EV and do
not serve DL's hubs (here named EVnonDL). Of note, the correlation between flight
frequency rates and cancellation rates for EVDL remained negative and very similar to
the correlation rate for EV, over the 2006 - 2009 time period. However, these coefficients
are not statistically significant at 5% significance level (except for EVnonDL in 2007) ,
implying that there isn't a strong linear dependence between flight frequencies and
cancellation rates for EV flights for 2006 - 2010 period, irrespective of whether or not
they serve DL hubs. In 2010, the correlation coefficient for EVDL increased and became
positive and close to that of DL in 2010, which implies that EV now cancelled more
45
flights on higher-frequency carrier-segments than it did on lower-frequency carriersegments. Notably, DL's merger with NW was finalized in 2010, and the carrier began
operating under the DL name. In terms of carrier-segments served by regional carriers
on behalf of DL, EV had the highest share (33.73%) in 2010 which may imply that EV's
operations in 2010 may have been greatly influenced by the merger between DL and
NW.
MQ and AA
60.00%
46
50.00%
40.00%
30.00%
20.00%
10.00%
-4-AA
W0400
-1-MQAA
-*MQnonAA
0.00%
2006
2006
Correlation p-value
24.93% 0.00
AA
11.39% 0.16
MQAA
0.16% 0.28
MQnonA
2007
2007
Correlation p-value
50.55% 0.00
22.85% 0.00
1.48% 0.41
2008
2009
2008
Correlation p-value
26.87% 0.00
13.53% 0.10
0.09% 0.59
2010
2009
Correlation p-value
34.71% 0.00
15.59% 0.06
2.13% 0.37
2010
Correlation p-value
10.00% 0.08
8.08% 0.31
6.43% 0.14
Figure 2.2.4: Correlation between average flight frequency and average flight
cancellation rates for AA, MQAA, and MQnonAA
Figure 2.2.4 above represents the trend in correlation between frequency rates and
cancellation rates of all carrier-segments operated by AA, those operated by MQ out of
hub airports for AA (here named MQAA), and those carrier-segments that were not
operated out of AA hubs (here named MQnonAA). Of note, the correlation between
46
flight frequency rates and cancellation rates for MQAA remained positive and had a
similar trend to that of AA, over the 2006 - 2010 time period. The correlation between
frequency rates and cancellations rates for the MQnonAA carrier-segments remained
close to zero (and statistically insignificant at 5% level) throughout time period, which
suggests that the cancellation decisions made by MQ on carrier-segments it did not
operate out of AA's hubs were not influenced by the carrier-segments'
flight
frequencies.
OH and DL
40.00%
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
-+-DL
0.00%
-U-OHDL
-5.00%
-'-OHnonDL
-10.00%
2006
2006
Correlation p-value
34.75%
DL
0.00
-5.07%
0.57
OHDL
4.25%
0.62
OHnonD:
2007
2007
Correlation p-value
26.28% 0.00
14.36%
0.10
13.35% 0.13
2008
2009
2008
Correlation p-value
28.87%
0.00
14.41%
0.12
7.53%
0.45
2010
2009
Correlation p-value
19.74% 0.00
20.71% 0.06
27.24% 0.01
2010
Correlation p-value
22.16% 0.00
0.03
26.48%
28.01% 0.01
Figure 2.2.5: Correlation between average flight frequency and average flight
cancellation rates for DL, OHDL, and OHnonDL
Figure 2.2.5 above represents the trend in correlation between frequency rates and
cancellation rates of all carrier-segments operated by DL, those operated by OH out of
hub airports for DL (here named OHDL), and those carrier-segments that were not
operated out of DL hubs (here named OHnonDL). Of note, the correlation between
flight frequency rates and cancellation rates for OHDL was close and had a similar
47
trend to that of OHnonDL, over the 2006 - 2010 time period. Interestingly, the statistical
significance level showed a general increasing trend with time.
00
and DL
40.00%
30.00%
20.00%
10.00%
0.00%
- -DL
-M-OODL
--*-OOnonDL
-10.00%
-20.00%
-30.00%
2007
2006
2009
2008
2010
2006
2007
2008
2009
2010
Correlation p-value
Correlation p-value
Correladon p-value
Correlation p-value
Correlation p-value
DL
00DL
34.75%
-6.61%
0.00
0.61
26.28%
-8.53%
0.00
0.47
00nonDL
-8.70%
0.12
-10.41%
0.04
28,87%
6.43%
-3.67%
0.00
0.57
0.46
19.74%
-8.44%
-3.05%
0.00
0.51
0.55
22.16%
-19.12%
12.69%
0.00
0.11
0.00
Figure 2.2.6: Correlation between average flight frequency and av erage flight
cancellation rates for DL, OODL, and OOnonDL
48
00
and UA
50.00%
40.00%
30.00%
20.00%
10.00%
00'000000
.000000
AOL
0.00%
-*-UA
-0
_001
Ak
-4-OOUA
-10.00%
-4--OOnonUA
-20.00%
-30.00%
2006
2006
Correlation p-value
46.50%
0.00
UA
0.21
-10.84%
OOUA
0.35
00nonUA
-5.89%
2007
2007
Correlation p-value
42.94%
0.00
-16.88%
0.05
-7.78%
0.17
2008
2009
2008
Correlation p-value
30.65%
0.00
-5.53%
0.50
-2.26%
0.69
2010
2009
Correlation p-value
0.00
36.33%
0.80
-0.25%
-9.07% 0.13
2010
Correlation p-value
31.76%
0.00
15.01%
0.02
6.37%
0.22
Figure 2.2.7: Correlation between average flight frequency and average flight
cancellation rates for UA, OOUA, and OOnonUA
Figures 2.2.6 and 2.2.7 above represent the correlation between frequency rates and
cancellation rates for 00 compared with those of the two main legacy carriers it serves
- DL and UA. Figure 2.2.6 represents the trend in correlation between frequency rates
and cancellation rates of all carrier-segments operated by DL, those operated by 00 out
of hub airports for DL (here named OODL) and those operated by 00 but not out of
DL hubs (here named 00nonDL). Between 2006 and 2009, the correlation of 00nonDL,
DL, and OODL had similar shape in trend, and in particular that of OODL was similar
to that of 00nonDL. In 2010, the correlation increased for both 00nonDL and DL but
fell for OODL. The statistical significance for the correlations remained generally low
for OODL and 00nonDL compared with that for legacy carrier DL.
Figure 2.2.7 represents the trend in correlation between the frequency rates and
cancellation rates of all carrier-segments operated by UA, those operated by 00 out of
49
hub airports for UA (here named OOUA), as well as those carrier-segments not
operated out of UA hubs (here named OOnonUA). The trend in correlation for OOUA
remained similar to that of OOnonUA throughout the time period. As before, the
statistical significance for the correlations remained generally low for OOUA and
OOnonUA compared with that for the legacy carrier UA.
XE and CO
50.00%
40.00%
/*
30.00%
77_1 s
20.00%
10.00%
0.00%
-+-Co
-10.00%
-u-XECO
-20.00%
--
XEnonCO
-30.00%
2006
2006
Correlation p-value
CO
16.50% 0.02
24.47% 0.00
XECO
XEnonCO 21.97% 0.01
2007
2007
Correlation p-value
14.43% 0.04
24.90% 0.00
42.60% 0.00
2009
2008
2008
Correlation p-value
14.69% 0.04
6.03% 0.45
37.18% 0.00
2010
2009
Correlation p-value
2.26% 0.78
-7.09% 0.42
-1.23% 0.89
2010
Correlation p-value
10.93% 0.16
-19.59% 0.02
-16,83% 0.05
Figure 2.2.8: Correlation between average flight frequency and average flight
cancellation rates for CO, XECO, and XEnonCO
Figure 2.2.8 above represents the trend in correlation between frequency rates and
cancellation rates of all carrier-segments operated by CO, those operated by XE out of
hub airports for CO (here named XECO), and carrier-segments not operated out of CO's
hubs by XE (here named XEnonCO). Of note, the correlation between flight frequency
rates and cancellation rates for XECO was close and had a similar trend to that of
50
XEnonCO, over the 2006 - 2010 time period. Unlike for the other regional carriers, the
correlation for XE was positive from 2006 - 2008, after which it decreased and became
negative. However, the negative coefficients were not statistically significant. In general,
the strength of relationship between flight frequencies and cancellation rates seems to
be decreasing with time.
YV and US
60.00%
50.00%
40.00%
30.00%
20.00%
A**'p-
10.00%
0.00%
-10.00%
-+-US
-20.00%
-h*-YVUS
-30.00%
'NUYVnonUS
-40.00%
2007
2006
US
YVUS
YVnonUS
2008
2009
2006
2007
2008
Correlation p-value
Correlation p-value
Correlation
40.32%
-3.14%
-8.68%
0.00
0.82
0.22
36.49%
11.31%
-22.43%
0.00
0.37
0.00
p-value
50.65% 0.00
-14.01%
-29.17%
0.34
0.00
2010
2009
2010
Correlation p-value
Correlation p-value
47.67%
10.78%
-15.36%
0.00
0.48
0.05
43.92%
10.85%
-31.83%
0.00
0.47
0.01
Figure 2.2.9: Correlation between average flight frequency and average flight
cancellation rates for US, YVUS, and YVnonUS
51
YV and UA
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
--
-10.00%
UA
-*r-YVUA
-20.00%
-9YVnonUA
-30.00%
2006
UA
YVUA
YVnonUA
2007
2008
2009
2010
2006
2007
2008
2009
2010
Correlation p-value
Correlation p-value
Correlation p-value
30.65% 0.00
Correlation p-value
Correlation p-value
31.76% 0.00
46.50%
-9.50%
-6.53%
0.00
0.49
0.36
42.94%
-15.28%
-13.26%
0.00
0.26
0.07
-12.77%
-24.54%
0.38
0.00
36.33%
19.16%
-20.22%
0.00
0.10
0.02
-1.09%
-16.21%
0.96
0.121
Figure 2.2.10: Correlation between average flight frequency and average flight
cancellation rates for UA, YVUA, and YVnonUA
Figures 2.2.9 and 2.2.10 above represent the correlation between frequency rates and
cancellation rates for YV compared with those of the two main legacy carriers it serves US and UA. Figure 2.2.9 represents the trend in correlation between frequency rates and
cancellation rates of all carrier-segments operated by US, those operated by YV out of
hub airports for US (here named YVUS), and those carrier-segments not operated out of
US hubs by YV (here named YVnonUS).. According to Figure 2.2.9, the correlation
values for the YVUS carrier-segments were similar and close to the correlation values
for the YVnonUS carrier-segments.
Figure 2.2.10 represents the trend in correlation between the frequency rates and
cancellation rates of all carrier-segments operated by UA, those operated by YV out of
hub airports for UA (here named YVUA), and those not operated out UA's hubs by YV
52
(here named YVnonUA). According to Figure 2.2.10, between 2006 and 2008, and in
2010, the correlation values for the YVUA carrier-segments were similar and close to the
correlation values for the YVnonUA carrier-segments.
From the analysis above, a few general observations can be made. First, the flight
frequencies and cancellation rates seem to have the expected positive relationship for
the legacy carriers. This implies that the higher the frequency of flights on a carriersegment the lower is the likelihood of cancellation, all else being equal. So the legacy
carriers exhibit a tendency to cancel flights on lower-frequency carrier-segments than
those on higher-frequency carrier-segments. Second, we observe that this tendency is a
lot weaker for the regional carriers. In most cases, we found little or no correlation
between the flight frequency and cancellation rates for the regional carriers. This might
suggest that the cancellation decisions for regional carriers are driven more by other
considerations than the ease of rebooking as reflected by the flight frequency on the
carrier-segment.
In addition to flight frequency, load factors also play an important role in airline
decisions about whether or not to cancel a flight. All else held equal, airlines would
prefer to cancel flights with lower load factors rather than those with higher load factors
because low load factors ease the passenger recovery process. That is, low load factors
result in fewer passengers needing to be reaccommodated, and offer more seats on later
flights for recovery.
To investigate why some of the regional carriers had negative correlation between the
flight frequency rates and cancellation rates, we use adjusted R-square values (adjusted
coefficient of multiple determination) to test the hypothesis that load factors affected the
cancellation decisions more than frequency rates among the regional carriers. This
analysis is done by comparing regression models in which cancellation rates are the
dependent variable and both average frequency and average load factors are the
independent variables, and comparing the R-square values with those of regression
models in which either of average frequency rates or average load factors is the only
independent variable. That is, the three regression models are as follows:
1. Regression model in which the dependent variable is the average cancellation
rate for each carrier-segment and the independent variables are the average
53
flight frequency for each carrier-segment and the average load factor for each
carrier-segment.
2. Regression model in which the dependent variable is the average cancellation
rate for each carrier-segment and the independent variable is the average flight
frequency rate for each carrier-segment.
3. Regression model in which the dependent variable is the average cancellation
rate for each carrier-segment and the independent variable is the average load
factor for each carrier-segment.
This comparison is done for the regional carriers and the legacy carriers.
For each group of carriers, we aggregate all the carrier-segments (unique carrier-origindestination combinations) for all the carriers within the carrier group. Each carrierorigin-destination combination represents a single observation. The dependent variable
for the regression models is obtained as an average cancellation rate corresponding to
each observation. In addition, each of the explanatory variables in each regression
model is calculated by averaging the appropriate value across the flights corresponding
to the observation. For our calculations, we choose to consider only the carrier-segments
that had an average flight frequency greater than or equal to one flight per day.
For each year, we use the times change in the adjusted R-squared values to compare the
relative impact of the explanatory variables. We define the times change in adjusted Rsquared due to inclusion of frequency as an explanatory variable, ARsq, as shown in
Equation 1 below:
sq__ RsqLF&F-RsqLF
F
-RsqLF
whereby RsqLF&, represents the adjusted R-squared value obtained in the regression
model that has both the average load factors and average frequencies of the
combinations as the explanatory variables for cancellation rates, and RsqLF represents
the adjusted R-squared value obtained in the regression model that has average load
factors as the only explanatory variable for flight cancellation rates. We define the times
change in adjusted R-squared due to the inclusion of load factor as an explanatory
variable,
, as shown in Equation 2 below:
Rsq
(RsqLF&F- RsqF)
RsqF
54
(2)
whereby RsqLF&F is as described above, and RsqF represents the adjusted R-squared
value obtained in the regression model that has average flight frequency as the only
explanatory variable for flight cancellation rates.
Table 2.2.4 below presents the adjusted R-square values obtained for the three models
for the legacy carriers:
LEGACIES
2006
2007
Total Number
of
Observations
1529
RsqLF
RsqLF&F
F
sq
LF
q
0.2828
0.1144
0.1329
1.4707
1.1271
0.1409
0.2017
1.4313
0.6987
0.0686
0.0994
1.3438
1501
0.1609
0.2929
0.0942
0.3975
0.3074
1203
0.3230
0.2034
0.0450
0.2691
1280
Adjusted R-square values for the three models for the legacies
0.6184
3.2216
4.3834
1590
0.3426
2008
2009
2010
Table 2.2.4:
RsqF
The last two columns of Table 2.2.4 give the times change in the adjusted R-square
values that is achieved by either the inclusion of average frequency rates or average
load factors of the carrier-segments as an independent variable, respectively. In 2006 2008, the inclusion of average frequency rates resulted in a higher increase in the
adjusted R square value than did the inclusion of average load factors. This means that
in 2006 - 2008, average flight frequency levels on the carrier-segments influenced the
legacy carriers' decision on cancellation more than the average load factors on the
carrier-segments did, all else held equal. In 2009 and 2010, the results were different,
that is, the inclusion of average load factor values for each carrier-segment as an
explanatory variable resulted in higher adjusted R-square values than did the inclusion
of average flight frequency rates. Thus, in 2009 and 2010, the flight cancellation
decisions made by the legacy carriers were influenced more by the load factors on the
carrier-segments than the average flight frequency rates on the carrier-segments. This
observation can be attributed to a recent trend observed in the airline industry of
"capacity discipline". Capacity discipline has evolved as a response of the economic
55
downturn in 2008 whereby legacy carriers have shifted their attention away from
focusing their efforts on capacity expansion and have begun to prioritize on high yields
and load factors (Wittman and Swelbar, 2013). This trend has resulted in legacy carriers
increasingly cutting service on routes that have lower load factors and yields.
Table 2.2.5 below presents the adjusted R-square values obtained for the three models
for the regional carriers:
REGIONALS
Total Number
of
Observations
RsqLF
RsqLF&F
0.0418
1172
0.0435
2006
0.0144
0.0196
2007
1426
0.0444
0.0449
2008
1312
2009
1203
0.1074
0.1076
0.1017
0.1004
2010
1250
Table 2.2.5: Adjusted R-square values for the three models
RsqF
Rsq
q
0.0058
0.0402 6.5348
0.3547 2.6268
0.0054
-0.0001 -0.0120 324.1490
-0.0007 -0.0020 149.6500
-0.0006 -0.0120 173.6780
for the regional carriers
As before, the last two columns of Table 2.2.5 above give the times increase in the
adjusted R-square values that is achieved by either the inclusion of average frequency
rates or average load factors of the carrier-segments as an independent variable,
respectively. From 2006 - 2010, the inclusion of average load factors for each carriersegment as an explanatory variable resulted in a much higher increase in the adjusted
R-square value than did the inclusion of average flight frequency rates for the carriersegments. The difference was especially stark in the years 2008 - 2010. In fact in 2008 2010, the inclusion of average flight frequency as an explanatory variable resulted in a
slight decrease in the adjusted R-square values of the models. Hence, the flight
cancellation decisions made by the regional carriers were influenced more by the load
factors on the carrier-segments than by the average flight frequencies of the carriersegments. That is, load factors are a critical part of the cancellation decision made by the
regional carriers, more so than frequencies.
56
2.3 Carrier Effect
In addition to the various scheduling and operational practices of the different carriers
having an impact on flight cancellation rates, airport congestion levels and weather
patterns also play a huge role in affecting cancellation rates. Airports across the U.S.
have different congestion levels depending on market demand and facilities, and are
located in varying climatic conditions. In addition, the distribution of operations at
airports varies significantly among carriers and hence, it is expected that the higher
cancellation rates experienced by some carriers can be attributed to differences in where
the bulk of their operations are. For example, DL which has a primary hub in ATL is
likely not forced to cancel as many flights as AA, which has a primary hub in ORD (due
to persistent weather/capacity issues at ORD). Hence, it is unclear how much of the
difference between the two carriers' cancellation rates is as a result of network
differences (i.e., differences in where the airlines operate their flights). In this section,
we use a metric presented in Vaze (2011) called carriereffect, in an effort to measure the
relative impact of carrier-specific decisions on cancellations. As we observed in the
previous sections of this chapter, the airport-specific variations and the carrier-specific
variations in cancellation rates are intertwined due to carrier-airport interdependence,
and it is important to try and separate the two in order to better understand the true
cause of the variation observed in cancellation rates. Carrier effect metric attempts to
make this separation. We then analyze the trend in each carrier's carrier effect over the
2006 -2010 time period and the corresponding impact on the respective carrier's
cancellation rate.
First, for each airport a, the baseline cancellation rate, Pa, is set to equal the actual
cancellation rate for all scheduled departures that are operated by non-hub carriers at
the airport. A non-hub carrier at an airport is defined as a carrier whose operations at
the airport constitute less than 10% of its total operations. The baseline cancellation rate
is calculated by eliminating the hub carriers in order to normalize for the additional
operational flexibility that these carriers have at the airports. Equation 1 below defines
Pa. Na and Ca represent the total number of planned departures and departure
cancellations, respectively for carrier c at airport a, and 7f~ represents the set of nonhub carriers at airport a.
57
aE~
C
Pa =ceg-gN
The carrier effect, EC, for each carrier c is then calculated by dividing the carrier's actual
number of cancellations at all airports by the baseline number of cancellations, that is,
the carrier effect is a ratio of the carrier's actual cancellations to the expected number of
cancellations if the carrier did not experience any additional flexibility at its hub
airports. The baseline number of cancellations for each carrier c, at airport a, is
calculated by multiplying the total number of scheduled departures by the carrier, Nca
by the baseline cancellation rate, fa. For each carrier, we define the baseline number of
cancellations for each carrier c as the sum of the numbers of baseline cancellations
across all airports as in Equation 3 below.
Baseline number of cancellations for carrier c =La Ne X
fa
(2)
We define carrier effect in Equation 3 below:
Ec =
ca)
C
(ZaC)
Za NC X pa
(3)
A value of carrier effect less than 100% is more desirable as it indicates that the carrier
experienced fewer cancellations than the baseline based on the distribution of flight
departure airports. Table 2.3.1 below lists the actual and baseline cancellation rates, the
carrier effect, and the rank based on both the actual cancellation rate and the carrier
effect for each carrier, in 2006.
58
Carrier
Actual
Cancellation
Rate
Baseline
Cancellation Rate
1.92%
0.42%
B6
2.02%
0.53%
CO
1.88%
0.82%
FL
1.73%
1.00%
F9
1.88%
1.12%
NW
1.68%
1.09%
US
1.20%
0.81%
WN
0.68%
0.48%
HA
1.93%
1.52%
DL
1.89%
1.57%
AA
2.17%
2.05%
UA
2.10%
2.20%
XE
2.34%
2.48%
EV
2.32%
2.47%
OH
1.38%
1.52%
AS
1.95%
2.34%
00
2.53%
3.63%
MQ
2.12%
3.13%
YV
Table 2.3.1: Carriereffects in 2006
Carrier
Effect
Actual
Cancellation
Rate Rank
Carrier
Effect
Rank
21.85%
26.28%
43.60%
57.73%
59.47%
64.83%
67.65%
70.58%
78.88%
83.21%
94.31%
104.90%
106.15%
106.28%
110.20%
119.94%
143.37%
147.95%
1
3
5
6
8
7
4
2
9
11
12
13
16
15
9
14
18
17
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
According to Table 2.3.1 above, many of the differences in rankings according to the
actual cancellation rate and the carrier effect are small. 13 out of the 18 carriers listed
above have a difference in the two ranks of two or less (two have a difference of zero,
eight have a difference of one, and three have a difference of two). In 2006, B6 was the
carrier with the lowest actual cancellation rate despite having its hub at JFK, one of the
busiest airports. This is attributed to the carrier's policy of commitment to reliable
service by opting to operate flights with a delay rather than cancelling them for the
schedule's convenience (Brennan and Morgan, 2013). Hence, B6 also ranks first in terms
of carrier effect. HA has the second lowest actual cancellation rate among all the
carriers. This is because it predominantly operates in the Hawaiian region and hence is
not affected by adverse weather conditions. Therefore, when we factor in the
operational advantage that HA experiences (using carrier effect), the carrier drops six
positions to 8 th in rank. According to the results above, AS ranked 9th in terms of
59
cancellation rate, with an actual cancellation rate of 1.52%. Similar to HA, AS operates
predominantly operates out of airports such as SEA, PDX, LAS, and ANC which are not
affected by adverse weather conditions and hence have lower cancellation rates. When,
we factor in this operational advantage that AS experiences because of the airports
where it operates, the carrier drops six positions to
15 th
in rank.
All the regional carriers have predominantly high carrier effects, which is consistent
with these carriers having higher cancellation rates than carriers in the other three
categories.
Table 2.3.2 below lists the actual and baseline cancellation rates, the carrier effect, and
the rank based on both the actual cancellation rate and the carrier effect for each carrier,
in 2007.
Carrier
Actual
Cancellation
Rate
0.41%
F9
0.91%
CO
0.99%
FL
0.85%
WN
1.37%
DL
0.42%
HA
1.94%
B6
1.89%
NW
1.84%
US
2.43%
UA
2.48%
XE
1.60%
AS
3.07%
9E
2.37%
00
3.12%
EV
0.84%
AQ
2.83%
AA
3.78%
OH
4.22%
MQ
3.83%
YV
Table 2.3.2: Carrier effects
Baseline
Cancellation Rate
1.83%
2.38%
2.20%
1.43%
2.23%
0.68%
2.69%
2.45%
2.04%
2.48%
2.46%
1.50%
2.87%
2.08%
2.73%
0.72%
2.36%
3.12%
3.18%
2.51%
in 2007
60
Carrier
Effect
Actual
Cancellation
Rate Rank
Carrier
Effect
Rank
22%
38%
45%
59%
61%
62%
72%
77%
90%
98%
101%
107%
107%
114%
114%
117%
120%
121%
133%
153%
1
5
6
4
7
2
11
10
9
13
14
8
16
12
17
3
15
18
20
19
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
According to Table 2.3.2 above, 11 out of the 20 carriers have a difference in the two
ranks of 2 or less (4 of zero, 2 of one, and 5 of two). B6 dropped in rank in terms of
actual cancellation rate from 1st position in 2006 to l11
position in 2007. This is because
of the infamous Valentine's Day snow storm on 14t* February, 2007, that greatly
disrupted operations at JFK, forcing the airline to cancel more than half of its flights on
that day. B6 has the largest improvement in carrier effect rank compared to its actual
cancellation rate rank, because its cancellation rates at its two busiest airports (JFK and
BOS) were below the baseline totals at the airports (63% of the baseline total for BOS
and 81% of the baseline total for JFK). Excluding the non-continental carriers, WN has
the lowest actual cancellation rate, because it operates predominantly at airports with
low cancellation rates such as LAS, PHX, and MDW.
The carrier with the largest absolute change in rank is AQ which falls 13 positions from
3rd place to 1 6 th place. Like HA, AQ is a Hawaii-based carrier and hence is not largely
affected by adverse weather conditions. When we normalize for the airport effect, HA
also drops in rank (although less drastically than AQ) by four positions from 2"1 place
to 6 th place. Of note, data for AQ is missing from the table on 2006 data because the
airline ceased reporting its operations between 1 1 th November, 2001 and April, 2006.
AA's cancellation rate increased from 1.57% in 2006 to 2.83% in 2007, moving the carrier
from 11* position in terms of its actual cancellation rate in 2006 to
15
th
position.
Consequently, the airline's carrier effect worsened from 83.21% in 2006 to 120% in 2007,
that is, AA made 83.21% of the expected cancellations in 2006 but performed worse in
2007 by cancelling more flights than expected.
Table 2.3.3 below lists the actual and baseline cancellation rates, the carrier effect, and
the rank based on both the actual cancellation rate and the carrier effect for each carrier,
in 2008.
61
Carrier
Actual
Cancellation
Rate
F9
0.32%
NW
0.84%
FL
0.85%
CO
1.24%
DL
1.51%
EV
1.79%
US
1.45%
WN
1.03%
B6
1.63%
AS
1.42%
UA
2.34%
9E
2.71%
XE
2.67%
00
2.19%
HA
0.92%
OH
3.27%
MQ
3.74%
AA
2.88%
YV
3.62%
Table 2.3.3: Carrier effects
Baseline
Cancellation Rate
Carrier
Effect
Actual
Cancellation
Rate Rank
Carrier
Effect
Rank
1.43%
2.21%
2.15%
2.43%
2.26%
2.63%
1.99%
1.42%
2.24%
1.42%
2.24%
2.51%
2.42%
1.96%
0.81%
2.76%
2.79%
2.04%
2.32%
in 2008
22%
38%
39%
51%
67%
68%
73%
73%
73%
100%
104%
108%
110%
112%
113%
119%
134%
141%
156%
1
2
3
6
9
11
8
5
10
7
13
15
14
12
4
17
19
16
18
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
As in the previous years, in 2008, the differences in ranking according to the actual
cancellation rate and the carrier effect are quite small. Of the 19 carriers listed above, 13
have a difference in the two ranks of two or less (three have a difference of zero, five
have a difference of one, and five have a difference of two). NW had an improvement in
cancellation rate from 1.89% in 2007 to 0.84% in 2008 - it was the legacy carrier with the
lowest cancellation rate in 2008 and moved from 10th position to 2nd position in terms of
actual cancellation rate rank from 2007 to 2008. In turn, the carrier's carrier effect
improved from 77% in 2007 to 38% in 2008, that is, NW cancelled 77% of the baseline
cancellations in 2007, and only cancelled 38% of the baseline cancellations in 2008.
According to the results above, HA had a low cancellation rate of 0.92% and ranked 4 th
in terms of actual cancellation rate. However, after normalizing for the airport effect,
HA's rank drops to 15th position. In fact, the carrier had the biggest drop in rank among
62
all the carriers. AQ's bankruptcy and consequential demise in 2008 may have led to this
change observed in HA's normalized cancellation rate, as HA increased the number of
flights it operated in order to accommodate the increased demand (Las Vegas Review
Journal, 2008).
Among the regional carriers, EV had the largest improvement in actual cancellation
rate, from 3.12% in 2007 to 1.79% in 2008, and an improvement in actual cancellation
rate rank from 1 7 th position in 2007 to 1111 position in 2008. The carrier had 61 rank
based on carrier effect in 2008, and hence was the carrier with the largest improvement
in rank. EV had a carrier effect of 114% in 2007 and 68% in 2008. Hence, in 2007 the
carrier had more cancellations than the baseline while in 2008 it only cancelled 68% of
the baseline cancellations.
Table 2.3.4 below lists the actual and baseline cancellation rates, the carrier effect, and
the rank based on both the actual cancellation rate and the carrier effect for each carrier,
in 2009.
63
Carrier
Actual
Cancellation
Rate
Baseline
Cancellation Rate
HA
0.21%
0.80%
CO
0.53%
1.60%
NW
0.65%
1.58%
FL
0.77%
1.51%
F9
0.63%
1.17%
AS
0.86%
1.25%
1.12%
DL
1.56%
WN
0.76%
1.01%
US
1.24%
1.44%
B6
1.35%
1.47%
XE
1.77%
1.73%
9E
1.87%
1.79%
EV
2.07%
1.93%
00
1.42%
1.33%
UA
1.69%
1.47%
AA
1.68%
1.35%
YV
2.01%
1.54%
MQ
2.54%
1.82%
OH
3.40%
2.02%
Table 2.3.4: Carrier effects in 2009
Carrier
Effect
Actual
Cancellation
Rate Rank
Carrier
Effect
Rank
26%
33%
41%
51%
54%
69%
72%
75%
86%
92%
102%
105%
107%
107%
115%
124%
130%
139%
169%
1
2
4
6
3
7
8
5
9
10
14
15
17
11
13
12
16
18
19
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
According to the results in Table 2.3.4 above, in 2009, the differences between the
rankings according to actual cancellation rate and carrier effect were smaller than in the
previous three years, with 13 out of the 19 carriers listed above having a difference in
rank of two or less (six with a difference of zero, four with a difference of one, and three
with a difference of two) and all of the 19 carriers having a difference in rank of four or
less. HA had a decrease in actual cancellation rate from 0.92% in 2008 to 0.21% in 2009.
Consequently, the airline's carrier effect improved from 113% in 2008 to 26% in 2009.
Hence, although the carrier ranked highly in terms of actual cancellation rate in 2008
( 4 th
position), it cancelled more flights than would have been expected. But in 2009, the
airline only cancelled 26% of the baseline cancellations, thus placing it in ls position in
terms of carrier effect rank.
64
Table 2.3.5 below lists the actual and baseline cancellation rates, the carrier effect, and
the rank based on both the actual cancellation rate and the carrier effect for each carrier,
in 2010.
Carrier
Actual
Cancellation
Rate
0.08%
HA
F9
0.43%
CO
0.83%
FL
1.07%
AS
0.58%
US
1.55%
WN
1.03%
UA
1.46%
B6
2.04%
EV
2.35%
AA
1.69%
XE
2.11%
DL
2.03%
YV
1.97%
00
1.99%
MQ
2.76%
9E
2.93%
OH
3.82%
Table 2.3.5: Carrier effects
Baseline
Cancellation Rate
0.87%
1.15%
1.98%
2.11%
1.08%
2.05%
1.34%
1.89%
2.18%
2.49%
1.78%
2.18%
2.07%
1.80%
1.72%
2.28%
2.25%
2.44%
in 2010
Carrier
Effect
Actual
Cancellation
Rate Rank
Carrier
Effect
Rank
9%
37%
42%
51%
54%
75%
77%
77%
93%
94%
95%
97%
98%
109%
116%
121%
130%
157%
1
2
4
6
3
8
5
7
13
15
9
14
12
10
11
16
17
18
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
As can be observed from the results in Table 2.3.5 above, of the 18 carriers listed above,
14 had a difference in the two ranks of two or less (five had a difference of zero, three
had a difference of one, and six had a difference of two). EV is the carrier that had the
largest rank improvement, i.e. the rank based on actual cancellation rate minus rank
based on carrier effect, with a rank of 15 based on actual cancellation rate and a rank of
10 based on carrier effect.
Of note is that DL's actual cancellation rate increased from 1.12% in 2009 to 2.03% in
2010, and its carrier effect increased from 72% in 2009 to 98% in 2010, which suggests
65
that the carrier increased its cancellations relative to the baseline. In 2010, the merger
between DL and NW was completed and the two carriers began reporting jointly as DL.
NW's cancellation rate, however, remained low (below 2.0%) throughout the 2006 2009 time period and hence the increase in DL's cancellation rate in 2010 is not as a
result of NW's cancellation rates, but may rather be due to challenges that the two
carriers may have experienced in coordinating their operations.
From our results, we believe that the carrier effect is a better metric for evaluating the
cancellation performance of carriers as compared to the actual cancellation rate, because
it allows us to separate out the airport effect by normalizing each carrier's cancellation
rate based on the prevalent cancellation rates at the airports at which they operate.
Carrier effect achieves this by calculating the prevalent cancellation rate at an airport by
discounting the additional operational flexibility that a hub carrier might experience at
that airport.
Most of the major airlines in the U.S. operate either hub-and-spoke networks or have
focus airports at which a bulk of their operations is concentrated. Hence, most one-stop
passengers on these carriers make their connections at these airports and this
concentration of activity at the airports has an impact on the flight cancellation rates.
Airlines benefit from operational flexibility at these airports, based on the large number
of crew, gates, and aircraft at their disposal and hence have a better passenger recovery
process as compared to other carriers operating at these airports. In this section, we
measure the impact of this hub effect by extending the carrier effect analysis to measure
the hub-carriereffect, E"ub , for each carrier c at its hub.
In Equation 3 below, the hub-carrier effect for a given carrier c, is defined as the ratio of
the carrier's total number of cancellations at its hub, to the baseline number of
cancellations for the carrier at its hub, whereby C1"b represents carrier c's total number
of cancellations at its hub airport, Nub represents the total number of operations for
carrier c at its hub airport, and Phiib represents the baseline cancellation rate of non-hub
carriers at carrier c's hub.
E
EC
hub
ubhub
ccj"
NcuC
X
(3)
hub
As developed in Vaze (2011), we use the concept of carrier's coefficient of hubbing,
acu which is defined as the ratio of hub-carrier effect to carrier effect for each carrier
66
as shown in Equation 4 below. The coefficient of hubbing is used as a metric to
determine how much additional flexibility each carrier has at its primary hub
operations, compared to other non-hub carriers.
ae** =
Chdub
Ehub
-EC~
A lower coefficient of hubbing value represents higher additional flexibility for the
carrier at its hub as it means that the carrier received much more flexibility at its hub
than it did at the other airports where it predominantly operates.
Next, we present tables that list each carrier's main hub, percent of the carrier's
operations carried out at the hub, the values of carrier effect, hub-carrier effect, as well
as the coefficient of hubbing for each of the legacy and low-cost carriers over the 2006 2010 time period.
Carrier
Main
Hub
%Operations
main hub
at Carrier
Effect (Ec)
HubCarrier
Effect
Coefficient
of
Hubbing (achub)
(EChub)
AA
CO
DL
F9
FL
NW
UA
US
WN
B6
Table 2.3.6:
DFW
25.4%
0.83
0.59
IAH
28.5%
0.26
0.18
ATL
32.7%
0.79
0.62
DEN
49.0%
0.58
0.45
ATL
33.6%
0.44
0.31
MSP
22.1%
0.59
0.49
ORD
19.1%
0.94
0.57
CLT
13.5%
0.65
0.35
LAS
7.2%
0.68
0.65
JFK
31.0%
0.22
0.19
Effects of primary hub on cancellation rates in 2006
0.71
0.70
0.79
0.79
0.71
0.82
0.60
0.54
0.96
0.85
Table 2.3.6 above is a list of the values for the year 2006. According to the results, all of
the carriers have more than 13% of their operations at their main hubs, except for WN
which has its operations distributed across different airports and only 7.1% of its
operations in 2006 were concentrated at LAS, representing the highest concentration of
its operations. Hence, WN did not likely experience the operational flexibility afforded
to other carriers at their hubs and thus has the highest coefficient of hubbing value
among all the carriers listed above.
67
A similar conclusion can be drawn from Table 2.3.7 below which contains values for
2007.
Carrier
Main
Hub
%Operations
main hub
at Carrier
Effect (Ec)
HubCarrier
Effect
Coefficient of
Hubbing (achub)
(EChub)
AA
CO
DL
F9
FL
NW
UA
US
WN
B6
Table 2.3.7:
DFW
26.0%
1.20
0.91
IAH
28.5%
0.38
0.30
ATL
32.1%
0.61
0.34
DEN
48.7%
0.22
0.21
ATL
33.3%
0.45
0.32
MSP
22.5%
0.77
0.43
ORD
19.3%
0.98
0.62
0.58
CLT
15.3%
0.90
LAS
7.1%
0.59
0.57
JFK
30.7%
0.72
0.60
Effects of primary hub on cancellation rates in 2007
0.76
0.78
0.56
0.92
0.71
0.56
0.64
0.64
0.96
0.83
NW had the biggest improvement in the coefficient of hubbing value from 0.82 in 2006
to 0.56 in 2007. This is because of the increase in carrier effect value which suggests that
NW cancelled a higher percentage of the baseline cancellations in 2007 than it did in
2006. On further investigation, NW's average cancellation rate increased from 1.03% in
2006 to 1.70% in 2007, which may have contributed to the increase in its carrier effect
value. NW's hub-carrier effect value decreased from 0.49 in 2006 to 0.43 in 2007, which
suggests that the carrier made fewer cancellations relative to the baseline at its hub in
MSP. Furthermore, MSP, NW's hub airport, had the biggest increase in overall
cancellation rate among the top 50 airports in 2007, from an overall cancellation rate of
0.98% in 2006 to 2.27% in 2007 hence resulting in a higher baseline cancellation rate at
the airport which may have contributed to the decrease in NW's hub-carrier effect
value.
Table 2.3.8 below contains values for 2008:
68
Carrier
Main
Hub
%Operations
main hub
at Carrier
Effect (E,)
HubCarrier
Effect
Coefficient of
Hubbing (achub)
(EChub)
AA
CO
DL
F9
FL
NW
UA
US
WN
B6
Table 2.3.8:
DFW
IAH
ATL
DEN
ATL
MSP
ORD
CLT
LAS
JFK
Effects of
26.6%
1.41
1.26
28.7%
0.51
0.50
33.5%
0.67
0.63
50.0%
0.22
0.23
33.3%
0.39
0.44
22.6%
0.38
0.26
19.7%
1.04
0.68
17.2%
0.73
0.38
7.1%
0.73
0.90
27.0%
0.73
0.65
primary hub on cancellation rates in 2008
0.89
0.99
0.94
1.01
1.10
0.69
0.65
0.52
1.24
0.90
According to the results in Table 2.3.8 above WN, F9, and FL all had coefficient of
hubbing values that were greater than 1 in 2008. This means that these carriers did not
experience the operational flexibility afforded to other carriers at their hubs in terms of
cancellations. WN's carrier effect increased from 0.59 in 2007 to 0.73 in 2008, which
means that the airline cancelled more flights relative to the baseline in general in 2008
than in 2007. The airline's hub-carrier effect value increased from 0.57 in 2007 to 0.90 in
2008, while its carrier effect value increased from 0.59 in 2007 to 0.73 in 2008. This
means that the carrier's carrier effect value increased more than its hub-carrier effect.
Hence, WN made more cancellations relative to the baseline more so at its hub in LAS
than at the other airports where it operated and hence it did not experience additional
advantage at its hub.
F9's hub-carrier effect value increased only slightly from 0.21 in 2007 to 0.23 in 2008.
Although the carrier generally had cancellations well below the baseline at all the
airports (according to the carrier effect value) as well as at its hub in DEN (according to
the hub-carrier effect value), it did not experience any additional advantage at DEN. In
addition, there wasn't much difference in the carrier effect, hub-carrier effect, and
coefficient of hubbing values for F9 between 2007 and 2008.
FL had a decrease in its carrier effect value from 0.45 in 2007 to 0.39 in 2008, while the
airline's hub-carrier effect value increased from 0.32 in 2007 to 0.44 in 2008. This means
69
that although FL generally cancelled fewer flights relative to the baseline, the number of
cancellations it had relative to the baseline at its ATL hub increased and thus the carrier
appears to not have experienced any additional flexibility in ATL in 2008.
Table 2.3.9 below has the values for 2009:
Carrier
Main
Hub
%Operations
main hub
at Carrier
Effect (E,)
HubCarrier
Effect
Coefficient
of
Hubbing (achub)
(EChub)
AA
CO
DL
F9
FL
NW
UA
US
WN
B6
Table 2.3.9:
DFW
IAH
ATL
DEN
ATL
MSP
ORD
CLT
LAS
JFK
Effects of
28.0%
1.24
0.78
29.5%
0.33
0.30
35.3%
0.72
0.47
49.4%
0.54
0.47
31.6%
0.51
0.42
22.7%
0.41
0.27
19.5%
1.15
0.99
18.6%
0.86
0.72
7.1%
0.75
0.91
25.0%
0.92
0.69
primary hub on cancellation rates in 2009
0.63
0.91
0.66
0.87
0.83
0.66
0.87
0.84
1.21
0.75
According to the results in Table 2.3.9 above, WN was the only carrier that had a
coefficient of hubbing value greater than 1, and the values of the carrier effect, hubcarrier effect, and coefficient of hubbing were similar to those from 2008. As in previous
years, the carrier did not experience as much operational flexibility at LAS as did other
carriers at their hubs because only 7.1% of its operations were concentrated at LAS. That
is to say, WN's hub at LAS did not provide the carrier with the advantage that is typical
of hubs , i.e., availability of a large number of crews, gates, and aircraft at the hubbing
carrier's disposal in order to help ease the passenger recovery process for the carrier.
The results in Table 2.3.9 suggest that in 2009, all the other carriers except WN had
coefficient of hubbing values that were less than 1 which means that they all
experienced additional operational advantage at their hubs.
Table 2.3.10 below provides the values for 2010:
70
Carrier
Main
Hub
%Operations
main hub
at Carrier
Effect (Ec)
HubCarrier
Effect
Coefficient of
Hubbing (achub)
(EChub)
AA
DFW
28.6%
0.95
0.83
CO
IAH
29.5%
0.42
0.37
DL
ATL
23.6%
0.98
0.91
F9
DEN
46.3%
0.37
0.33
FL
ATL
28.4%
0.51
0.45
UA
ORD
18.7%
0.77
0.66
US
CLT
20.2%
0.75
0.65
WN
LAS
6.9%
0.77
0.65
B6
JFK
22.3%
0.93
0.69
Table 2.3.10: Effects of primary hub on cancellation rates in 2010
0.87
0.88
0.93
0.89
0.89
0.85
0.86
0.85
0.74
According to the results in Table 2.3.10 above, all the carriers had coefficient of hubbing
values less than 1, including WN. In fact, different from the previous four years' results,
WN did not have the highest coefficient of hubbing value. WN's carrier effect remained
approximately constant from 2009 to 2010, but the carrier's hub-carrier effect value
improved from 0.91 in 2009 to 0.65 in 2010. The carrier's cancellation rate increased
from 0.47% in 2009 to 0.79% in 2010. On further investigation, we find that the baseline
cancellation rate at LAS was 0.84% in 2010, while the overall cancellation rate at the
airport in the same year was 0.70%. This means that although WN's overall cancellation
rate increased in 2010, the airline's performance at its hub in LAS was much better than
for other airlines operating at the hub. DL had a value of 0.93, which was an increase
from 0.66 in 2009. Both the carrier effect and hub-carrier effect values increased which
suggest that the airline cancelled more flights relative to the baseline in 2010 than in
2009 at its ATL hub and also at other airports in the system. In 2010, the merger
between DL and NW was completed and the two carriers began operating under the
Delta name that year. This could explain the significant change in DL's carrier effect and
hub-carrier effect values. We further investigate DL's values at MSP, which was NW's
main hub airport. In 2009, DL's operations at MSP constituted only 0.5% of the carrier's
total operations that year while in 2010 its operations at MSP constituted 4.4% of its
total operations. If we treat MSP as DL's hub for the two years (accounting only for
DL's operations at the airport), we find that the hub-carrier effect value in 2009 was 0.31
(the number of flights cancelled by DL was well below the baseline number of
71
cancellations) and in 2010 it was 0.86 which means that after DL and NW merged, the
operational flexibility that had been enjoyed by NW at MSP reduced for the joint
carrier. Hence, the merger between DL and NW played a huge role in affecting DL's
cancellation rate in 2010.
We notice that both FL and DL have their highest coefficient of hubbing values in 2008
and 2010 - 1.01 in 2008 and 0.89 in 2010 for FL, and 0.94 in 2008 and 0.93 in 2010 for DL.
This is important because the two carriers share a hub at ATL. Interestingly, the two
carriers have a similar trend in their coefficient of hubbing values as shown in Figure
2.3.1 below:
Trend in achub values for FL and DL
1.2
1
0.8
-*-FL
0.6
--
DL
0.4
0.2
0
2006
2007
2008
2009
2010
Figure 2.3.1: Coefficient of hubbing values for FL and DL
According to Figure 2.3.1 above, of the two carriers at ATL, DL experienced more
operational flexibility except in 2006 and 2010.
The results of the analysis done over the 2006 - 2010 time period suggests that there is a
substantial operational advantage for flights departing from a carrier's primary hub. To
further confirm this, we compare flights arriving into the primary hub with those
departing from it. In addition, we believe that the operational advantage afforded by
carriers at their hub airports results in the carriers being able to absorb flight delay at
their hubs. To confirm this intuition, we include an analysis of the overall percentage of
flights that suffer large delays, whereby we define a large delay as one which is greater
72
than or equal to 30 minutes, that is, a flight is considered to have suffered a large delay
if it arrives (departs) 30 minutes after when it was scheduled to arrive (depart). The
tables presented in this analysis provide these values for the 2006 - 2010 time period.
Table 2.3.11 below presents the results obtained for 2006
Cancellation
Rate
Carrier Primary For
Hub
Flights
from
Main
Hub
AA
DFW
1.4%
CO
IAH
0.3%
DL
ATL
1.4%
F9
DEN
1.0%
FL
ATL
0.7%
NW
MSP
0.7%
UA
ORD
2.4%
US
CLT
0.7%
WN
LAS
0.6%
B6
JFK
0.5%
Total
1.1%
Table 2.3.11: Cancellation
the primary hub in 2006
For
Flights
into
Main
Hub
1.5%
0.3%
1.6%
1.0%
0.8%
0.9%
2.7%
0.9%
0.6%
0.5%
1.2%
rates and
% of flights w/ at Least 30
Minutes of Delay
%
Increase
For
For
%
Flights Flights Increase
from
into
Main
Main
Hub
Hub
4.0%
14.4%
10.5%
-27.3%
16.6%
14.8%
10.3%
-30.5%
10.6%
14.6%
12.9%
-11.2%
3.3%
9.5%
7.2%
-24.4%
18.2%
17.6%
16.0%
-8.9%
29.7%
13.1%
8.8%
-32.5%
10.6%
20.7%
16.3%
-21.0%
26.8%
12.3%
9.2%
-25.8%
-1.8%
13.4%
11.4%
-14.5%
8.8%
17.8%
17.1%
-3.6%
10.8%
15.0% 12.0%
-19.9%
large delays for flight arrivals and departures at
According to the results in Table 2.3.11 above, the average cancellation rate for all
flights departing from the carrier's hubs (1.1%) is 10.8% lower than the cancellation rate
for all flights arriving into the carrier's hubs (1.2%). Furthermore, for each of the carriers
listed in Table 2.3.11 above except for WN, the cancellation rates for flights departing
from the hubs were lower than the cancellation rates for flights arriving into the hubs.
The different relationship observed for WN can be attributed to the low concentration
of WN's flights at LAS - only about 7% of WN's departures were concentrated at LAS
and hence the carrier did not experience much operational flexibility at the airport.
We further analyze the difference in cancellation rates by carrier group, that is, by
comparing the legacy carriers with the low-cost carriers. In 2006, the cancellation rate
73
for all flights departing from the legacy carriers' hubs (1.2%) was 11.5% lower than the
cancellation rate for all flights arriving at the hubs (1.4%). For the low-cost carriers, the
cancellation rate for all flights departing from their hubs (0.67%) was 7.3% lower than
the cancellation rate for all flights arriving at their hubs (0.72%). When we eliminate
WN from the calculation for low-cost carriers, the decrease in cancellation rate becomes
11.1%, which is very similar to that of the legacy carriers. Hence, both carrier groups
experienced very similar operational flexibility from their hub airports in 2006.
Table 2.3.11 also lists the percentage of flights entering and exiting the main hubs that
suffered large delays. According to the results obtained, the overall percentage of flights
that experienced large delays arriving into the hub airports (12.0%) was 19.9% lower
than the flights departing from the primary hub (15.0%). This means that carriers are
able to absorb flight delay and still operate the departing flights out of the primary hubs
(as against cancelling them), and hence this is consistent with the cancellation rates for
flights into the hub being higher than those for flights leaving the hub. Of all the carriers
listed above, B6 has the smallest decrease in the number of flights that experienced a
large delay. The reason for this is that B6's hub is at JFK, which is a highly congested
airport - in 2006, JFK had an overall cancellation rate of 1.75% and ranked 17th in terms
of cancellation rate among the top 50 busiest airports and 4h among the 10 main hubs
listed above (after ORD, DEN, and DFW).
Table 2.3.12 below provides the values for 2007:
74
% of flights w/ at Least 30
Minutes of Delay
Carrier Primary For
For
%
For
For
%
Flights Flight Increase
Flights Flight Increase
Hub
from
into
from
into
Main
Main
Main
Main
Hub
Hub
Hub
Hub
15.1%
-21.5%
2.5%
19.3%
3.0%
3.1%
AA
DFW
10.7% -23.4%
13.9%
0.5%
22.8%
0.4%
CO
IAH
10.5% -17.2%
12.7%
0.9%
1.1%
21.0%
DL
ATL
12.6%
9.3% -26.3%
DEN
0.4%
0.5%
27.8%
F9
11.8% -17.5%
14.3%
0.8%
1.0%
19.6%
FL
ATL
18.1%
13.5% -25.5%
12.2%
1.6%
MSP
1.5%
NW
22.0%
17.2% -21.5%
8.5%
3.2%
3.5%
UA
ORD
13.8% -27.0%
18.8%
25.3%
1.5%
1.2%
CLT
US
11.9%
9.4% -20.7%
0.7%
-0.9%
0.7%
WN
LAS
3.5%
20.4%
21.1%
2.3%
JFK
2.4%
2.4%
B6
9.2%
16.4%
13.2% -19.9%
Total
1.6%
1.7%
Table 2.3.12: Cancellation rates and large delays for flight arrivals and departures at
the primary hub in 2007
Cancellation
Rate
According to the results in Table 2.3.12 above, the average cancellation rate for all
flights departing from the carrier's hubs (1.6%) is 9.2% lower than the cancellation rate
for all flights arriving into the carrier's hubs (1.7%). Similar to the previous year, WN
was the only carrier in the above list for which the cancellation rate for flights leaving
the hub airport was higher than the cancellation rate for flights arriving into the hub
airport.
Table 2.3.12 also lists the percentage of flights entering and exiting the main hubs that
suffered large delays. According to the results obtained, the overall percentage of flights
that experienced large delays arriving into the hub airports (13.2%) was 19.9% lower
than the flights departing from the primary hub (16.4%). This means that more flights
with more than 30 minutes of delay leave the hub airports than enter them, suggesting
that, rather than canceling more often, carriers are able to absorb more delay at their
hubs than at other airports and hence these results are consistent with the cancellation
rates. Of all the carriers listed above, B6 was the only carrier for which the number of
flights with large delays departing from the hub airport was smaller than the number of
75
flights with large delays arriving at the hub airport. This can be attributed to the high
congestion level at JFK, and in particular the increased congestion at the airport in the
wake of the Valentine's Day storm on Feb 14h, 2007. Furthermore, the overall
cancellation rate at JFK increased from 1.75% in 2006 to 2.8% in 2007, moving the
airport's rank from 17th position in 2006 to 6 th position in 2007 in terms of cancellation
rate among the top 50 busiest airports, and to 2nd position among the 10 main hubs
listed above (after ORD). Of note is that for B6's operations at JFK, the percentage of
flights with large delays departing and arriving at JFK stayed approximately the same
in both 2006 and 2007. However, the cancellation rates changed tremendously from
around 0.5% in 2006 to about 2.4% in 2007, yet the cancellation rate remained quite
similar for flights departing and arriving at JFK for both years. Table 2.3.13 below
provides the values for 2008:
Cancellation
Rate
% of flights w/ at Least 30
Minutes of Delay
Carrier Primary For
For
%
For
For
%
Hub
Flights Flight
Increase
Flights Flight
Increase
from
into
from
into
Main
Main
Main
Main
Hub
Hub
Hub
Hub
AA
DFW
2.9%
3.0%
3.1%
17.0%
12.4%
-27.0%
CO
IAH
1.1%
1.2%
8.5%
13.6%
10.7%
-21.2%
DL
ATL
1.2%
1.3%
11.9%
13.4%
11.6%
-13.9%
F9
DEN
0.3%
0.4%
24.4%
10.8%
8.3%
-23.2%
FL
ATL
0.8%
1.0%
20.0%
14.3%
12.2%
-14.8%
NW
MSP
0.6%
0.8%
31.0%
13.6%
9.5%
-29.6%
UA
ORD
3.3%
3.6%
8.7%
21.7%
16.8%
-22.4%
US
CLT
0.9%
1.2%
32.6%
11.9%
7.8%
-34.3%
WN
LAS
1.1%
1.0%
-11.4%
11.9%
10.9%
-8.1%
B6
JFK
2.0%
2.1%
6.9%
17.7%
17.5%
-1.2%
1Total
1.6%
1.7%
8.4%
14.8%
11.8%
-2O.1%
Table 2.3.13: Cancellation rates and large delays for flight arrivals and departures at
the primary hub in 2008
According to the results in Table 2.3.13 above, the overall cancellation rate for all flights
departing from the main hubs (1.6%) was 8.4% lower than the cancellation rate for all
flights arriving at the main hubs (1.7%) in 2008. The individual carriers also had higher
76
cancellation rates for flights arriving into their hubs than for flights leaving from their
hubs, except for WN whereby the cancellation rate for flights departing from LAS was
11.4% higher than the cancellation rate for flights arriving at LAS. This further confirms
the less operational flexibility that WN received at LAS due to its lower concentration of
operations there.
The results on flights that suffered large delays suggest that for all carriers, the overall
percentage of flights that suffered a large delay was greater for hub departures than for
hub arrivals. This is consistent with the higher cancellation rates for arrivals than
departures, which suggests that air carriers were able to absorb delay at their hub
airports and still operate the departing flights out of the hub. This same effect is also
observed at the individual carrier levels, even for B6 although the percentage difference
is much smaller compared to that of the other carriers. For B6, the large delays into and
out of the JFK hub are very balanced, with only a 1.2% difference between the two.
Table 2.3.14 below provides values for 2009:
% of flights w/ at Least 30
Minutes of Delay
Cancellation
Rate
Carrier Primary For
For
Hub
Flights Flight
into
from
Main
Main
Hub
Hub
1.8%
AA
DFW
1.7%
0.3%
CO
IAH
0.3%
0.9%
1.0%
ATL
DL
DEN
0.6%
0.7%
F9
0.8%
0.9%
FL
ATL
0.7%
0.5%
MSP
NW
ORD
2.2%
2.5%
UA
1.1%
CLT
0.8%
US
0.6%
0.6%
LAS
WN
1.8%
1.7%
B6
JFK
1.2%
1.0%
Total
Table 2.3.14: Cancellation rates and
the primary hub in 2009
%
Increase
For
For
%
Flights Flight Increase
from
into
Main
Main
Hub
Hub
-30.1%
5.3%
15.0%
10.5%
7.9%
-24.3%
10.4%
33.2%
6.9%
11.6%
12.4%
16.3%
13.1%
9.0%
-31.2%
18.9%
14.9%
-6.5%
15.8%
16.0%
-9.2%
10.0%
11.1%
41.3%
-31.8%
9.6%
14.0%
12.2%
-30.8%
11.6%
8.0%
33.2%
-9.5%
8.0%
8.9%
-1.9%
4.4%
14.3%
14.9%
7.1%
10.6%
-16.1%
12.7%
12.9%
large delays for flight arrivals and departures at
77
According to the results in Table 2.3.14 above, in 2009 the overall cancellation rate for
flights arriving at the carriers' hubs (1.0%) was 12.9% higher than the overall
cancellation rate for flights departing from the carrier's hubs. Similar to the previous
years, WN was the only carrier in 2009 for which the cancellation rate for departures
from LAS was higher than the cancellation rate for the arrivals into the airport.
In terms of flights that suffered large delays, the overall percentage of flights with large
delays arriving into a primary hub (10.6%) was 16.1% lower than that for the flights
departing from the primary hub. This same effect is also observed at the individual
carrier level for all carriers except for B6 and DL. For both B6 and DL, the percentage of
flights with large delay into and out of the hub was very balanced. For DL, the
cancellation rates for both the flights into the hub and out of the hub decreased from
2008 to 2009 (from 1.2% in 2008 to 0.9% in 2009 for flights from ATL, and from 1.3% in
2008 to 1.0% in 2009 for flights into ATL). The difference between the cancellation rates
for arrivals and for departures at ATL increased from 11.9% in 2008 to 16.3% in 2009,
but the difference in large delays between the arrivals and departures decreased and
changed sign. Table 2.3.15 below contains values for 2010:
Cancellation
% of flights w/ at Least 30
Rate
Minutes of Delay
Carrier Primary For
For
%
For
For
%
Hub
Flights Flight
Increase
Flights Flight
Increase
from
into
from
into
Main
Main
Main
Main
Hub
Hub
Hub
Hub
8.4%
-31.7%
12.2%
1.6%
5.0%
AA
DFW
1.6%
8.9%
6.6%
-25.9%
0.3%
0.4%
19.7%
IAH
CO
11.2%
-7.5%
2.1%
2.3%
10.6%
12.1%
DL
ATL
7.3%
-31.8%
10.7%
0.4%
18.5%
F9
DEN
0.4%
-4.6%
9.9%
9.4%
17.0%
ATL
1.0%
1.2%
FL
8.4%
-22.8%
10.9%
2.1%
13.3%
UA
ORD
1.8%
-35.6%
10.6%
6.8%
1.2%
19.4%
CLT
1.0%
US
9.1%
-20.9%
6.1%
11.5%
0.5%
0.6%
WN
LAS
-12.3%
15.1%
13.3%
2.8%
5.1%
JFK
2.6%
B6
10.3%
11.4%
9.1%
-20.5%
Total
1.4%
1.5%
Table 2.3.15: Cancellation rates and large delays for flight arrivals and departures at
the primary hub in 2010
78
According to the results obtained in Table 2.3.15 above, the overall cancellation rate for
flights departing from the primary hubs (1.4%) was 10.3% lower than the overall
cancellation rate for flights arriving at the primary hubs (1.5%). In 2010, this effect was
also observed at the individual carrier level for all carriers including for WN. Notably,
for WN, the cancellation rate for flights arriving at and departing from LAS remained
approximately constant and similar to the values in 2009, but the percentage difference
in large delays between arrivals and departures was quite large (20.9%), when
compared to2009 (9.5%).
In the analysis of flights that suffered a large delay, the overall percentage of flights
with large delays arriving into a primary hub (9.1%) was 20.5% lower than that for the
flights departing from the primary hub. This effect was also observed for all carriers
including B6 for which we had observed an opposite effect in the past years. Of note,
the cancellations into and out of JFK are both the highest for B6 in 2010 over all the
other years. Furthermore, the overall cancellation rate at JFK in 2010 was 3.39%, which
was the airport's highest cancellation rate over the 2006 - 2010 time period (the airport's
overall cancellation rate was 1.75% in 2006, 3.24% in 2007, 2.69% in 2008, and 2.19% in
2009). Note that the cancellation rate at JFK was higher in 2010 even when compared
with the year 2007 which had the most system-wide cancellations and delays.
Our analysis of comparing cancellation rates and percentage of flights with a large
delay for flights departing from primary hubs with those for flights arriving at primary
hubs for the 2006 - 2010 time period confirms the intuition that primary hubs provide
the respective carriers with additional operational flexibility. The results obtained over
the entire period are consistent with cancellation rates being higher for arrivals into the
hubs than for departures and the flight delay results suggest that carriers are able to
absorb more delay to departures at their primary hubs compared with departures from
other airports.
2.4: Modelling Cance1ations
In this sub-section, we present regression models to predict flight cancellation rates
using the various insights that have been discussed so far in this chapter. First, for each
year from 2006 -
2010 we aggregate the
data into carrier-origin-destination
combinations, that is, each combination of carrier, origin and destination represents a
79
single unique observation. In order to eliminate issues relating to sample size, we only
consider carrier-origin-destination observations that have at least 100 flights.
The dependent variable in our models is the cancellation rate for each individual
observation, that is, the average cancellation rate for each carrier-origin-destination
combination. We present four regression models for this analysis; the third model
builds on the combination of both the first and second model, and the fourth model
builds on the third model. Hence, by using this incremental approach we are able to
capture the relative impact of the inclusion of the variables. Each of the explanatory
variables is calculated as an average across the flights corresponding to each
observation.
As previously discussed, cancellation rates vary substantially across airports and
carriers. Different airports have different congestion levels and this affects their flight
cancellation rates - it is expected that airports with higher levels of congestion have
higher flight cancellation rates, and this is further exacerbated by bad weather.
Furthermore, as previously discussed, different carriers have different operating
policies and this is reflected in varying cancellation rates among the carriers. Hence, our
first two models investigate the effect of airport and carrier factors on cancellation rates
over the 2006 - 2010 time period.
The first regression model attempts to predict flight cancellation using the level of
congestion at both the origin airport and the destination airport for each carrier-origindestination combination. We define the level of congestion at the origin (destination) as
the percentage of scheduled flights which departed (arrived) having 15 minutes or more
of delay, that is, we define the congestion level as 1 minus the on-time performance of
the airport whereby the on-time performance of the origin (destination) airport is
defined as the percentage of scheduled flights that departed (arrived) having less than
15 minutes of delay.
Table 2.4.1 below presents the results of the first regression model for 2006 - 2010:
80
Model
1
1
1
Model
1
1
1
Model
1
1
1
Model
1
1
1
2006
Estimat
Parameter Description
e
-8.24EIntercept
03
% of Flights Not On-Time at 5.50EOrigin
02
% of Flights Not On-Time at 5.07EDestination
02
Std
Error
8.78E04
3.13E03
3.25E03
PValue
0.00
0.00
0.00
2007
Estima
Parameter Description
te
-9.48EIntercept
03
% of Flights Not On-Time at 5.85E02
Origin
% of Flights Not On-Time at 6.51E02
Destination
Std
Error
1.10E03
4.OOE03
4.OOE03
PValue
0.00
2008
Estima
Parameter Description
te
-8.49EIntercept
03
% of Flights Not On-Time at 6.40E02
Origin
% of Flights Not On-Time at 6.19E02
Destination
Std
Error
1.04E03
4.26E03
4.13E03
PValue
0.00
2009
Estima
Parameter Description
te
8.24EIntercept
04
% of Flights Not On-Time at 9.56E03
Origin
% of Flights Not On-Time at 5.34E-
Std
Error
7.90E04
3.47E03
3.29E-
PValue
0.30
Destination
03
02
81
Adjusted R-square
Value
0.1235
Adjusted R-square
Value
0.1073
0.00
0.00
Adjusted R-square
Value
0.1002
0.00
0.00
0.01
0.00
Adjusted R-square
Value
0.0512
Model
1
1
1
2010
Estima
Std
P-
Adjusted R-square
te
Error
Value
Value
2.52E03
% of Flights Not On-Time at -9.95EOrigin
03
% of Flights Not On-Time at 8.66E-
1.02E03
4.04E03
4.79E-
0.01
0.0500
Destination
03
Parameter Description
Intercept
02
0.01
0.00
Table 2.4.1: Estimation results for model 1 (airport congestion) for the 2006 - 2010
time period
According to the results in Table 2.4.1 above, for 2006 - 2009, the coefficient estimates
for the congestion levels at the origin as well as the destination airports are positive
which suggests that all else being equal, an increase in the congestion at either the
origin or the destination resulted in an increase in flight cancellation rates. In addition,
the coefficient estimates for the explanatory variables are statistically significant with at
least a 99% confidence level for all the five years. Additionally, the intercept was also
statistically significant with at least 99% confidence level for all years except 2009. In
2010, the coefficient of congestion at the origin was negative, which is non-intuitive.
Note that the models for 2009 and 2010 had lower explanatory power in general,
compared to those for 2009 and 2010. They had lower R-square values (about half of
those for 2006 - 2008); the coefficient estimates for the congestion at origin was lower
(less than 20% of those for 2006 - 2008); and had lower p-values for individual
parameter estimates than those for 2006 - 2008. These observations suggest that a large
part of the error in the cancellation rates in 2009 and 2010 was not explained by the
congestion level at either the origin or destination airport. So the flight cancellation
rates were less dependent on the congestion levels in 2009 - 2010 than in 2006 - 2008 To
further investigate this, we calculate the congestion level (in terms of the 15 minute ontime performance) for all flights operated in each year by mirroring the calculation of
congestion levels defined earlier. We gather the following results:
82
2006
75.1%
% of OnTime
Flights
% of Flights 24.9%
Not
On-
2007
73.6%
2008
76.1%
2009
79.2%
2010
79.9%
26.4%
23.9%
20.8%
20.1%
Time
Table 2.4.2: Distribution of on-time performance across the years
According to the results in Table 2.4.2 above, the congestion levels, as measured by the
percentage of total flights that arrived or departed within 15 minutes of delay, was
lower for 2009 and 2010 than for 2006 - 2008. Hence, this could explain the lower
dependence of cancellation rates on congestion levels that was observed in the first
regression model for 2009 and 2010.
In the second regression model, we attempt to predict the flight cancellation rates using
the type of carrier as the only explanatory variable. As discussed previously, we divide
all 20 ASQP carriers into 4 categories as follows: legacy carriers, low-cost carriers,
regional carriers, and non-continental carriers. Hence, for this model, we use three 0-1
dummy variables, one each for the legacy carriers, low-cost carriers, and the regional
carriers. Therefore, the coefficient estimates of the dummy variables measure the effects
of the policies of each of the three carrier groups with respect to those of the noncontinental carriers. Table 2.4.3 below shows the estimation results for the second
regression model for the 2006 - 2010 time period:
Model
Parameter Description
2006
Estimate
2
Intercept
1.72E-02
2
Legacy Carrier Dummy
5.58E-03
2
Low-Cost
Dummy
Regional
2
Carrier -1.18E-02
Carrier 9.62E-03
Dummy
Std
Error
1.72E03
1.74E03
1.77E03
1.74E03
83
PValue
0.00
0.00
0.00
0.00
Adjusted R-Square
Value
0.2650
Model
Parameter Description
2007
Estimate
2
Intercept
1.51E-02
2
Legacy Carrier Dummy
1.47E-03
2
Low-Cost
Carrier -7.34E-03
Dummy
2
Regional
Carrier
1.58E-02
Parameter Description
2008
Estimate
Intercept
1.51E-02
2
Legacy Carrier Dummy
1.54E-04
2
Low-Cost
Carrier -7.14E-03
Dummy
Regional
Adjusted R-Square
Value
0.1925
0.00
0.00
2.28E-
0.00
03
2
2
PValue
0.00
03
Dummy
Model
Std
Error
2.26E03
2.29E03
2.31E-
Std
P-
Adjusted R-Square
Error
Value
Value
2.23E03
2.26E03
2.28E-
0.00
0.1710
0.00
0.00
03
Carrier
1.33E-02
Dummy
2.25E-
0.00
03
2009
Model
Parameter Description
Estimate
2
Intercept
1.16E-02
2
Legacy Carrier Dummy
1.17E-03
2
Low-Cost
1.54E-04
Carrier
Dummy
2
Regional
Std
P-
Adjusted R-Square
Error
Value
Value
1.66E03
1.69E03
1.70E-
0.00
0.1833
0.00
0.00
03
Carrier
9.25E-03
Dummy
1.68E03
84
0.00
Model
Parameter Description
2010
Estimate
2
Intercept
7.23E-03
2
Legacy Carrier Dummy
7.51E-03
2
Low-Cost
1.89E-03
Carrier
Dummy
2
Regional
Std
P-
Adjusted R-Square
Error
Value
Value
1.96E03
1.99E03
2.00E-
0.00
0.1743
0.00
0.01
03
Carrier
1.86E-02
Dummy
1.98E-
0.00
.03
Table 2.4.3: Estimation results for model 2 (carrier types) for the 2006 - 2010 time
period
According to the results in Table 2.4.3 above, the estimated coefficient for the low-cost
carrier dummy was the lowest, followed by that of the legacy carrier dummy, and the
regional carrier dummy had the highest coefficient for all the years in the time period.
The coefficient for the low-cost carrier dummy was negative from 2006 - 2008 which is
consistent with the carrier group having the lowest cancellation rates among the four
groups during this time period; in 2009 and 2010, the low-cost carriers had a higher
cancellation rate than the non-continental carriers. Furthermore, the positive coefficient
of the legacy carrier dummy is consistent with the legacy carriers having a higher
cancellation rate than the non-continental carriers in the 2006 - 2010 time period. The
coefficient associated with the regional carrier dummy was positive and the highest in
value throughout the period, which is consistent with the regional carriers having the
highest cancellation rate among all the carrier groups during the time period. All the
coefficient estimates, including the estimates of the intercepts, are statistically
significant with at least a 99% confidence level. In addition, the adjusted R-squared
values for each of the years are higher than the adjusted R-square values obtained in the
first regression model, which implies that the carrier-specific variables explain a larger
part of the variation in the cancellation rates than the airport-specific variables.
The third regression model builds on the first and second model; that is, we combine
the airport and carrier factors from the previous two models and attempt to predict the
cancellation rates. Table 2.4.4 below shows the estimation results for the third model
over each year:
85
2006
Estimat
e
2.34E-03
Model
Parameter Description
3
Intercept
3
% of Flights Not On-Time at 4.99E-02
Origin
% of Flights Not On-Time at 2.71E-02
Destination
Legacy Carrier Dummy
-7.23E03
Low-Cost Carrier Dummy
-1.43E02
Regional Carrier Dummy
5.68E-03
3
3
3
3
Model
3
3
3
Std
Error
1.76E03
2.85E03
2.95E03
1.67E03
1.69E03
1.67E03
2007
Parameter Description
Estimat
e
Intercept
-7.32E03
% of Flights Not On-Time at 5.33E-02
Origin
% of Flights Not On-Time at 5.68E-02
Std
Error
2.30E03
3.66E03
3.90E-
Destination
03
3
Legacy Carrier Dummy
3
Low-Cost Carrier Dummy
3
Regional Carrier Dummy
-4.51E03
-9.55E03
1.07E-02
86
2.20E03
2.20E03
2.18E03
PValue
0.18
Adjusted
RSquare Value
0.3302
0.00
0.00
0.00
0.00
0.00
PValue
0.00
0.00
0.00
0.04
0.00
0.00
Adjusted
RSquare Value
0.2727
Model
3
3
3
3
3
3
2008
Parameter Description
Estimat
e
Intercept
-6.85E03
% of Flights Not On-Time at 7.11E-02
Origin
Yo of Flights Not On-Time at 5.23E-02
Destination
Legacy Carrier Dummy
-5.78E03
Low-Cost Carrier Dummy
-1.16E02
Regional Carrier Dummy
8.22E-03
Model
Parameter Description
3
Intercept
3
o
2009
Estimat
e
1.39E-03
of Flights Not On-Time at 2.99E-02
Origin
3
% of Flights Not On-Time at
Legacy Carrier Dummy
3
Low-Cost Carrier Dummy
3
Regional Carrier Dummy
PValue
0.00
Std
Error
1.71E03
3.28E-
PValue
0.42
3.57E-02
3.05E-
87
1.65E03
1.66E03
1.65E03
R-
Adjusted
Square Value
0.2259
R-
0.00
0.01
0.00
0.00
0.00
0.00
03
-3.61E03
-8.55E03
6.40E-03
Adjusted
Square Value
0.266817
0.00
03
Destination
3
Std
Error
2.22E03
3.90E03
3.88E03
2.14E03
2.15E03
2.13E03
0.03
0.00
0.00
Model
Parameter Description
3
Intercept
3
% of Flights Not On-Time at
Origin
% of Flights Not On-Time at
3
2010
Estimat
Std
P-
Adjusted
e
Error
Value
Square Value
-3.31E03
2.74E-02
2.00E03
3.95E03
4.46E-
0.10
0.2121
5.72E-02
Destination
3
Legacy Carrier Dummy
R-
0.00
0.00
03
2.20E-03
1.97E03
Low-Cost Carrier Dummy
3
-5.43E2.OOE03
03
3
Regional Carrier Dummy
1.21E-02 1.97E03
Table 2.4.4: Estimation results for model 3 (combination
carrier types) for the 2006 - 2010 time period
0.26
0.01
0.00
of airport congestion and
According to the results in Table 2.4.4 above, the coefficient estimate for the legacy
carrier dummy became negative for the years 2006 - 2009. This suggests that the high
cancellation rate of the legacy carriers is as a result of the high congestion levels of the
airports between which they operate. For 2010, the coefficient for the legacy carrier
dummy was positive but insignificant hence there was no statistically significant linear
dependence of the mean of cancellation rates on the legacy carrier dummy variable. The
coefficient estimates for the low-cost carrier dummy variable were also negative and
statistically significant for all the years. Similar to the legacy carriers, these results
suggest that the low-cost carriers' cancellation rates were influenced by the congestion
level of the airports between which they operate. For all the years, the adjusted Rsquare value for the third model is less than the sum of the adjusted R-square values of
the first and second regression models. In addition, for all the years, the coefficient
estimates for the congestion levels at the origin and destination were all positive and
statistically significant, as expected. This is an improvement over those for the first
model where the relationship between airport congestion and cancellations rate was
confounded by other effects due to the simplistic nature of that model. This means that
in the first model, these airport-specific variables were capturing some carrier-specific
effects on cancellation rates and this may further explain the negative sign obtained in
the model for 2010 data. The third model illustrates that the airport congestion levels
88
and the carrier type dummies capture very different aspects of the variation in
cancellation rates and the signs and relative magnitudes of these effects become
intuitively reasonable once the two effects are separated out.
The fourth regression model builds on the third model by introducing three more
variables, namely, a 0-1 dummy that indicates whether the origin airport of the
observation is the primary hub for the corresponding carrier, the average daily
scheduled frequency for each carrier-origin-destination combination, and the average
load factor for each unique observation. We use T-100 Domestic Segments database to
calculate the average values of the daily scheduled frequency and load factor for each
combination of carrier, origin, and destination in our data. The results from the fourth
model are presented in Table 2.4.5 below.
2006
Model
Parameter Description
Estimate
4
Intercept
1.97E-02
4
% of Flights Not On-Time at 5.75E-02
Std
Error
2.14E03
2.69E-
Origin
03
4
o
of Flights Not On-Time at
3.38E-02
Destination
2.78E-
PValue
0.00
0.00
0.00
03
4
Legacy Carrier Dummy
4
Low-Cost Carrier Dummy
4
Regional Carrier Dummy
-1.12E03
-8.38E03
1.30E-02
4
4
Dummy for Primary Hub at
Flight Origin
Average Frequency
-8.76E03
5.92E-04
4
Average Load Factor
-3.75E02
89
1.71E03
1.73E03
1.72E03
4.40E04
5.20E05
1.57E03
0.01
0.00
0.00
0.00
0.00
0.00
Adjusted
RSquare Value
0.4350
Model
4
4
4
4
4
4
4
4
4
2007
Parameter Description
Estimate Std
Error
Intercept
7.92E-03 2.76E03
% of Flights Not On-Time at 6.34E-02 3.50EOrigin
03
% of Flights Not On-Time at 6.80E-02 3.69EDestination
03
Legacy Carrier Dummy
-6.58E2.28E03
03
Low-Cost Carrier Dummy
-3.68E2.28E04
03
Regional Carrier Dummy
2.07E-02 2.27E03
Dummy for Primary Hub at -1.01E5.65EFlight Origin
02
04
Average Frequency
7.67E-04 6.68E05
Average Load Factor
-4.16E1.95E02
03
Model
Parameter Description
4
Intercept
4
4
2008
Estimate
-5.44E03
% of Flights Not On-Time at 7.31E-02
Origin
% of Flights Not On-Time at 5.83E-02
Std
Error
2.53E03
3.84E03
3.80E-
Destination
03
4
Legacy Carrier Dummy
4
Low-Cost Carrier Dummy
4
Regional Carrier Dummy
4
Dummy for Primary Hub at -8.44EFlight Origin
03
-4.87E04
-6.11E03
1.50E-02
90
2.28E03
2.29E03
2.29E03
5.67E04
PValue
0.00
Adjusted
RSquare Value
0.3592
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
PValue
0.03
0.00
0.00
0.00
0.01
0.00
0.00
Adjusted
RSquare Value
0.3178
4
Average Frequency
6.30E-04
4
Average Load Factor
-1.39E02
Model
Parameter Description
2009
Estimate
4
Intercept
3.01E-02
4
% of Flights Not On-Time at 4.41E-02
Origin
% of Flights Not On-Time at 4.49E-02
Destination
-1.27ELegacy Carrier Dummy
03
-5.98ELow-Cost Carrier Dummy
03
8.65E-03
Regional Carrier Dummy
4
4
4
4
4
4
Dummy for Primary Hub at -5.56E03
Flight Origin
2.84E-04
Average Frequency
4
Average Load Factor
-4.78E02
Model
Parameter Description
2010
Estimate
4
Intercept
2.13E-02
4
%Yo of Flights Not On-Time at 3.67E-02
Origin
0%0 of Flights Not On-Time at 6.10E-02
Destination
-5.41ELegacy Carrier Dummy
03
4
4
91
6.82E05
9.80E04
0.00
Std
Error
1.81E03
2.97E03
2.73E03
1.49E03
1.50E03
1.50E03
4.03E04
4.75E05
1.28E03
PValue
0.00
Std
Error
2.34E03
3.80E03
4.25E03
1.90E03
PValue
0.00
0.00
RAdjusted
Square Value
0.3903
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Adjusted
RSquare Value
0.2898
4
Low-Cost Carrier Dummy
-3.28E03
1.37E-02
1.94E0.01
03
4
Regional Carrier Dummy
1.92E0.00
03
4
Dummy for Primary Hub at -7.08E5.14E0.00
Flight Origin
03
04
4
Average Frequency
2.55E-04 6.14E0.00
05
4
Average Load Factor
-3.74E1.68E0.00
03
02
Table 2.4.5: Estimation results for model 4 (combination of airport congestion, carrier
types, frequency, load factor, and origin hub dummy) for the 2006 - 2010 time period
The results in Table 2.4.5 above suggest that the observations were consistent
throughout the years. The coefficient estimates for the airport congestion variables and
the carrier type dummies do not change substantially compared to the third regression
model. Both airport congestion level coefficient estimates were positive and statistically
significant, as in the third model which means that an increase in congestion levels at
either the origin or destination resulted in an increase in flight cancellations. Once the
airport congestion level, hubs at origin airports, frequency, and load factor levels are
adjusted for in our model, we find that the ranking in terms of cancellation rates among
the four carrier groups becomes clearer; in 2007 and 2010, the low-cost carrier group
had the lowest cancellation rate while in 2006, 2008, and 2009 the legacy carrier group's
cancellation rate was the lowest among the four carrier groups. This means that after
adjusting for the variables in the fourth model, neither of the legacy nor low-cost
carriers had a clear advantage over the other across the years. The non-continental US
carriers tend to have a higher adjusted cancellation rate than legacy and low-cost
carriers; and the regional carriers have the highest adjusted cancellation rate among the
four groups of carriers. The results suggest that the regional carriers' high cancellation
rates are not explained by any of the other factors that we include in the fourth model.
The coefficient estimates for the dummy for the primary hub at the origin airport
remained negative throughout the years, which is consistent with our observations
earlier that the hubs provide the corresponding hub carriers with operational advantage
for departing flights resulting in lower cancellation rates. In addition, the coefficient
estimates for the average flight frequency remained positive throughout the time period
while those for the average load factor remained negative, which is also consistent with
92
our results earlier that airlines are more likely to cancel flights on segments that have
higher frequency as well as flights that have lower load factors as this aids in the
reaccommodation process.
All the explanatory variables including the intercepts for all five years are statistically
significant with at least a 97% confidence level and the adjusted R-squared values are
significantly higher than those for the third model for all years, which means that three
additional variables are able to explain more variation in the cancellation rates.
93
Chapter 3
Analysis of Missed Connections over the 2006 - 2010 Time Period
Missed connections are the most significant cause of travel disruptions for one-stop
passengers. In calendar year 2007, missed connections were responsible for 57.2% of all
travel disruptions suffered by one-stop passengers and 40.9% of all the delays suffered.
In this section, we conduct a longitudinal study of missed connections across the 2006 2010 time period and analyze the most important factors affecting missed connections.
In our discussion, we use the term misconnection rate to refer to the ratio of the number
of one-stop passengers who missed their connections as a result of delays on the first
flight in their itinerary, to the total number of non-stop passengers. We express the
misconnection rate as a percentage. Notably, we choose to exclude one-stop passengers
whose planned itineraries had at least one canceled flight, from both the numerator and
the denominator of the expression for misconnection rates.
3.1 Misconnections at the Airport-Specific Level
Similar to what we did in our analysis on cancellation rates in the previous section, we
will analyze misconnection rates at both the airport level and the carrier level. At the
airport level we, again, consider the top 50 airports in the U.S. in terms of total number
of operations. 99.00% of all planned one-stop passenger connections during the 2006 2010 time period were made at these 50 airports, and they corresponded to 99.21% of all
missed passenger connections. We categorize passengers based on their connection
airports.
Next, we present a discussion of misconnection rates across the top 50 busiest airports
for each year within the 2006 - 2010 time period, and analyze the changes observed
across the years.
In Figure 3.1.1 below, we plot misconnection rates at the top 50 busiest airports in 2006,
arranged in decreasing order of misconnection rate.
94
Top 50 Airports' Misconnection Rates - 2006
7.00%
6.00%
"Top
50
Average
Misconnecti
on Rate
0'5.00%
04.00%
U
0
3.00%
I
U
22.00%
TimiItt
1.00%
uuu~~~~~~o~
w
i
l
l
IIl
i
l
l
i
Airport
Figure 3.1.1: Average misconnection rates across top 50 busiest airports in 2006
For 2006, the average misconnection rate across all airports in the U.S. was 3.44%, and
across the top 50 airports the average misconnection rate was 3.45%. EWR had the
highest misconnection rate (6.14%) and TPA had the lowest misconnection rate (1.60%).
16 of the top 50 airports had misconnection rates that were higher than the average
misconnection rate across the top 50 airports. There was a significant drop-off in
misconnection rate after the first 10 airports - after SEA (4.21%), no other airport had a
misconnection rate that was above 4.00%. The average misconnection rate at the 10
worst airports in terms of misconnection rates (4.98%) was more than 1.6 times greater
than the misconnection rate at the remaining 40 airports. Obviously, large delays to the
first flight in an itinerary are primarily responsible for misconnections. Hence, it is not
surprising that out of the nine worst airports in terms of misconnection rates, EWR,
ORD, LGA, JFK, PHL, SFO, and BOS were also the seven worst airports in terms of
average arrival delay that year. However, arrival delays are not the only cause for
misconnections
-
for instance, IAD which ranks second in terms of highest
misconnection rate (5.95%) has a much lower average arrival delay (14.82 minutes) than
LGA (19.70 minutes), ORD (20.78 minutes), SFO (16.58 minutes), JFK (18.21 minutes),
and BOS (16.32 minutes), all of which have lower average misconnection rates than
IAD. Another important factor that affects passenger connections is the length of
scheduled connection time. Obviously, the less time there is for a passenger to make a
95
connection, the more likely he/she is susceptible to a misconnection, and vice versa.
That is to say, longer connection times permit operational recovery and ensure
flexibility to contain disruptions (Jenkins et. al, 2012). We calculate the average
connection time for each of the top 50 airports in 2006; IAD had an average connection
time of 103.52 minutes making it the fourth-lowest average connection time across the
50 airports. LGA, ORD, SFO, JFK, and BOS all had higher average connection times
than IAD. This means that on average in 2006, one-stop passengers had less time to
make their connections at IAD than at these five airports, and this may explain the
higher misconnection rate at IAD.
In addition, TPA, which had the lowest
misconnection rate across the top 50 airports (1.60%), had the second-highest average
connection time among the top 50 busiest airports (134.06 minutes). In terms of average
arrival delay, TPA ranked
32nd
across the 50 busiest airports with an average of 11.82
minutes, which was a higher average arrival delay than 18 of the airports that had
higher misconnection rates. Thus, one-stop passengers had more time to make their
connections at TPA and this contributed to the airport's low misconnection rate. We
further explain this phenomenon later in this chapter in our discussion on schedule
banking.
Figure 3.1.2 below presents the misconnection rates across the top 50 busiest airports for
the 2007 calendar year.
Top 50 Airports' Misconnection Rates - 2007
8.00%
7.00%
6.00%
Top 50 Average
Misconnection
Rate
5.00%
o 4.00%
l 3.00%
0
o 2.00%
1.00%
0.00%
tI
I
I
I
Airport
Figure 3.1.2: Average misconnection rates across top 50 busiest airports in 2007
96
For 2007, the overall average misconnection rate across all the airports in the U.S. was
3.97% and across the top 50 airports, the average misconnection rate was 3.98%. This
was about a 15% increase in average misconnection rate from 2006 to 2007. The average
connection time across the 50 busiest airports remained fairly the same at 117.90
minutes in 2006 and 118.29 minutes in 2007, but the average flight delay across the top
50 airports increased from 13.63 minutes in 2006 to 15.03 minutes in 2007.Hence, the
increase in the average misconnection rate across the top 50 airports was likely a result
of the increase in average arrival delay at these airports. Among the top 50 airports,
EWR again had the highest misconnection rate of 7.55% and TPA again had the lowest
misconnection rate of 1.70%. In 2007, 20 of the top 50 busiest airports had misconnection
rates that were higher than the average misconnection rate across the top 50 airports.
After the two worst airports in terms of misconnection rates (EWR, and LGA, the latter
with a misconnection rate of 7.24%), there was a significant drop-off in misconnection
rates with the next airport, IAD having a misconnection rate of 6.12%. The average
misconnection rate at EWR and LGA (7.51%) is more than 1.9 times as high as the
average misconnection rate at the remaining 48 airports (3.95%).
Notably, the airports that had the largest changes in misconnection rates from 2006 to
2007 were IND (70.83% increase in misconnection rate), MIA (65.85% increase in
misconnection rate), CMH (62.36% increase in misconnection rate), and MEM (56.44%
increase in misconnection rate). IND's misconnection rate in 2006 was 2.31% (which was
well below the average misconnection rate across the top 50 busiest airports) and it
increased to 3.94% in 2007, and the airport's rank in terms of the highest misconnection
rates across the top 50 busiest airports changed from
3 6th
position to 2211,1 position. In
terms of arrival delay, however, the airport's performance improved from an average of
19.94 minutes of arrival delay in 2006 to an average of 15.21 minutes of arrival delay in
2007. IND's average connection time decreased from an average of 121.19 minutes in
2006 to 117.82 minutes in 2007, which implies that one-stop passengers had, on average,
more time to make their connections at IND in 2006 than they did in 2007. To further
investigate this, we analyze the connection time that was available to one-stop
passengers who connected at IND, based on the scheduled departure time of the first
flight in their itineraries. First, for all itineraries scheduled to connect at IND, we
calculate the average connection time afforded to the itineraries based on the scheduled
departure time of the first flight, for each hour across the two years and provide the
results in the second and third columns of Table 3.1.1 below.
97
Scheduled
Hour
of
Flight
Departure
12
midnight
6 a.m.
7 a.m.
8 a.m.
9 a.m.
10 a.m.
11 a.m.
12 noon
1 p.m.
p.m.
3 p.m.
Average
Connection
Time
(minutes)
in 2006
0.00
Average
Connection
Time
(minutes)
in 2007
65.00
% of Total
Connecting
Passengers
in 2006
% of Total
Connecting
Passengers
in 2007
0.00%
0.03%
97.56
102.26
114.36
110.45
124.90
108.12
127.89
95.57
97.46
74.14
97.30
100.18
115.20
109.05
124.34
106.06
127.21
92.75
95.06
73.58
1.03%
8.30%
13.00%
7.65%
18.72%
11.66%
17.04%
8.77%
5.48%
4.15%
3.61%
0.59%
0.00%
0.00%
2.70%
9.75%
6.86%
14.91%
8.92%
9.16%
4.31%
9.27%
5.06%
4.08%
16.12%
76.57
77.53
p.m.
5 p.m.
78.82
76.90
1.19%
p.m.
0.00
41.89
1.59%
7 p.m.
0.00
42.85
0.88%
3.10%
0.00%
121.60
0.00
IQp.m.
11 p.m.
0.00
102.67
0.00%
2.07%
Table 3.1.1: Average connection times by hour of day at IND and distribution of
connecting passengers in 2006 and 2007
According to Table 3.1.1 above, in both 2006 and 2007, the average connection time was
higher for flights that were scheduled to depart in the morning hours, from around 7
a.m. until noon, with the flights that were scheduled to depart during the noontime
hour having the highest scheduled connection time (127.89 minutes in 2006 and 127.21
minutes in 2007). We then analyze the distribution of one-stop passengers who made
their connections at IND for both 2006 and 2007, based on the scheduled departure time
of the first flight of the itinerary. The fourth and fifth columns in Table 3.1.1 above
provide a summary of the distribution of connecting passengers in 2006 and 2007,
respectively.
98
According to the fourth column of Table 3.1.1, in 2006, the highest share of one-stop
passengers who were scheduled to connect at IND (18.72%) were on flights scheduled
to depart between 10 a.m. and 11 a.m. According to the second column of Table 3.1.1,
these passengers had an average connection time of 124.90 minutes. In 2007, the highest
share of one-stop passengers scheduled to connect at IND (16.12%) were on flights
scheduled to depart between 4 p.m. and 5 p.m., which corresponds to an average
connection time of 76.57 minutes. Hence, the one-stop passengers who had connections
at IND had more time to make their connections at the airport in 2006 than in 2007 and
this helps explain the significant increase in the misconnection rate at the airport from
2006 to 2007.
MIA's misconnection rate in 2006 was 3.23% (which was below the average
misconnection rate across the top 50 busiest airports) and it increased to 5.36% in 2007
(which was above the average misconnection rate across the top 50 busiest airports).
MIA's rank in terms of highest misconnection rates across the top 50 busiest airports
worsened from 19t*, position in 2006 to 7t" position in 2007. In terms of arrival delay,
MIA's performance worsened from an average arrival delay of 13.41 minutes in 2006 to
an average arrival delay of 16.91 minutes in 2007; the airport's ranking in terms of
highest average arrival delay across the top 50 airports worsened from 1 4 th position in
2006 to 7t" position in 2007. Furthermore, MIA had the highest average connection time
(134.47 minutes) among the top 50 airports in 2006, and this decreased to 130.24 minutes
in 2007. Hence, the increase in misconnection rate observed at MIA from 2006 to 2007
can be explained by an increase in average arrival delay as well as some decrease in
average connection time at the airport.
The misconnection rate for CMH increased from 3.02% in 2006 to 4.90% in 2007, and the
airport's ranking in terms of highest misconnection rates across the top 50 airports
position in 2006 to 12t" position in 2007. In terms of average arrival
delay, the airport's performance worsened slightly from an average pf 15.40 minutes in
2006 to 16.84 minutes in 2007. The average connection time at CMH decreased from
worsened from
2 4 th
127.04 minutes in 2006 to an average of 121.19 minutes in 2007. Hence, the increase in
misconnection rate at the airport may have been as a result of an increase in average
delay at the airport as well as a decrease in average passenger connection time.
For MEM the misconnection rate in 2006 was 3.44% and it increased to 5.39% in 2007,
worsening the airport's rank across the top 50 busiest airports from 1 7 1h position in 2006
99
to 6t1h position in 2007. In terms of average arrival delay, MEM's performance worsened
slightly from an average of 12.43 minutes in 2006 to an average of 13.23 minutes in 2007.
Interestingly, MEM was the airport that had the lowest connection time across the top
50 airports, both in 2006 and 2007. Its average connection time in 2006 was 89.85
minutes and it decreased to 82.34 minutes in 2007. Thus, at MEM, one-stop passengers
were more susceptible to missing their connecting flights than at any other airport
among the top 50 in both 2006 and 2007, and more so in 2007.
In Figure 3.1.3 below, we present the misconnection rates across the top 50 busiest
airports for the 2008 calendar year.
Top 50 Airports' Misconnection Rates - 2008
8.00%
7.00%
w6.00%
-5.00%
0
w4.00%
Top 50 Average
Misconnection
Rate
03.00%
n
22.00%
-----------------
1.00%
n nnO/
Airport
Figure 3.1.3: Average misconnection rates across top 50 busiest airports in 2008
In 2008, the overall average misconnection rate in the U.S. was 3.70% and across the top
50 busiest airports it was 3.71%. LGA had, by far, the highest misconnection rate at
7.28% and SAT had the lowest misconnection rate at 1.82%. Of the top 50 busiest
airports, 18 had a misconnection rate that was higher than the overall average across the
group. Similar to previous years, it is not surprising that out of the 9 worst airports in
terms of misconnection rates, EWR, ORD, LGA, JFK, SFO, and MIA were the 6 worst
airports in terms of average arrival delay that year. Compared to the misconnection
rates in 2007, ONT had the highest increase in misconnection rate from an average
100
misconnection rate of 2.27% in 2007 to an average of 3.67% in 2008 (this was a 61.88%
increase in misconnection rate). CMH was the airport that had the highest decrease in
misconnection rate; its average misconnection rate decreased from an average of 4.90%
in 2007 to an average of 2.64% in 2008 (this was a 46.17% decrease in average
misconnection rate). The average arrival delay at ONT remained approximately the
same over the two years (10.32 minutes in 2007 and 10.50 minutes in 2008), but the
average connection time at the airport decreased from an average of 110.33 minutes in
2007 to an average of 100.42 minutes in 2008. Hence, the increase in average
misconnection rate observed at ONT may likely be due to the reduced average
connection time at the airport.
For CMH, the average arrival delay at the airport decreased by 18.59% from 16.84
minutes in 2007 to 14.20 minutes in 2008. Furthermore, the average connection time at
the airport increased from 120.29 minutes in 2007 to an average of 125.44 minutes in
2008. Thus, the decrease in average misconnection rate that was observed at CMH may
be attributed to both the decrease in average arrival delay as well as the increase in
average connection time at the airport.
In Figure 3.1.4 below, we present the misconnection rates across the top 50 busiest
airports for the 2009 calendar year.
Top 50 Airports' Misconnection Rates - 2009
7.00%
6.00%
A 5.00%
-
4.00%
0
3.00%
U
.E
2.00%
1.00%
Top 50 Average
Misconnection
Rate
MI
-
-
--
AAOL
%
Airport
Figure 3.1.4: Average misconnection rates across top 50 busiest airports in 2009
101
In 2009, the overall average misconnection rate in the U.S. was 3.24%, and it was 3.25%
across the top 50 busiest airports. 16 of the top 50 busiest airports had misconnection
rates that were higher than the average misconnection rate across the group. That year,
EWR had the highest average misconnection rate of 6.59% and MCI had the lowest
average misconnection rate of 1.16%. After the two worst airports in terms of
misconnection rates (EWR, and LGA which had an average misconnection rate of
6.10%), there was a significant drop-off, with the next airport, SFO, having an average
misconnection rate of 5.25%. To further illustrate the impact of arrival delay on
misconnections, of the 16 worst airports in terms of misconnections, EWR, LGA, SFO,
JFK, ATL, PHL, MIA, BOS, ORD, and DFW were the 10 worst airports in terms of
average arrival delay that year.
In 2009, the airport that had the highest increase in misconnection rate from 2008 was
HNL which had an average misconnection rate of 3.08% in 2008 and 4.35% in 2009 (this
was a 41.35% increase in average misconnection rate). At HNL, the average arrival
delay stayed approximately the same with the airport recording an average arrival
delay of 8.49 minutes in 2008 and 8.97 minutes in 2009. In terms of average connection
time, the airport observed a decrease from 113.93 minutes in 2008 to an average of
100.73 minutes in 2009. Thus, the increase in misconnection rate that was observed at
HNL from 2008 to 2009 can mostly be explained by the decrease in average connection
time that was observed.
MCI was the airport that had the highest decrease in misconnection rate from 2008 to
2009; the airport had an average misconnection rate of 2.02% in 2008 and of 1.16% in
2009 (this was a 42.69% decrease in average misconnection rate). In terms of average
arrival delay, there was a decrease at MCI, from 12.56 minutes in 2008 to an average of
10.14 minutes in 2009. Furthermore, the average connection time at the airport increased
from 118.97 minutes in 2008 to an average of 126.11 minutes in 2009. Hence, for MCI,
the observed decrease in misconnection rate from 2008 to 2009 may be attributed to
both a decrease in average arrival delay as well as an increase in average connection
time at the airport.
Figure 3.1.5 below presents the misconnection rates across the top 50 busiest airports for
the 2010 calendar year.
102
Top 50 Airports' Misconnection Rates - 2010
6.00%
5.00%
0
4.00%
3.00%
2.00%
1.00%
~WII44+fi+H*I*Fftn1tit
Top 50
Average
Misconnecti
on Rate
Airport
Figure 3.1.5: Average misconnection rates across top 50 busiest airports in 2010
In 2010, the overall average misconnection rate in the U.S. was 3.00% and 3.01% across
the top 50 busiest airports. This was the lowest average misconnection rate across the
entire 2006 - 2010 time period. In addition, the average arrival delay across the top 50
busiest airports was lowest in 2010 (10.99 minutes). Unlike in the previous years, the
two airports that had the highest average misconnection rate were SFO (5.47%) and
CVG (4.69%). This is because LGA and EWR, which had been the airports with the
highest misconnection rates, dropped to third and fourth positions, respectively. The
two New York airports recorded significant decreases in misconnection rates with
LGA's misconnection rate decreasing from 6.10% in 2009 to 4.63% in 2010 (a 24% drop
in misconnection rate) and EWR's misconnection rate decreasing from 6.59% in 2009 to
4.52% in 2010 (a 31% drop in misconnection rate). The decrease in these two airports'
misconnection rates is very high compared to the decrease in the average misconnection
rate across the 50 busiest airports, which decreased from 3.25% in 2009 to 3.01% in 2010
(only a 7% drop in misconnection rate). In terms of average arrival delay, both LGA
and EWR recorded significant decreases - LGA's average arrival delay decreased from
17.42 minutes in 2009 to 13.56 minutes in 2010 (a 22% drop in average arrival delay) and
the average arrival delay at EWR decreased from 24.59 minutes in 2009 to 16.70 minutes
in 2010 (a 32% drop in average arrival delay). The average connection times at the two
airports remained approximately the same, with LGA observing only a slight increase
from an average of 104.40 minutes in 2009 to 107.98 minutes in 2010, and EWR's
103
average arrival delay staying at 121 minutes for both years. Therefore, the significant
decrease in LGA's and EWR's average arrival delay contributed to the significant
decrease in misconnection rates observed at the two airports. TPA had the lowest
average misconnection rate of 1.22%. Out of the top 50 busiest airports, 18 had
misconnection rates that were higher than the average misconnection rate across the top
50 busiest airport. In addition to being the airport that had the highest misconnection
rate across the top 50 busiest airports, SFO was also the airport which suffered the
highest average arrival delay (18.34 minutes). Notably, CVG, which was the airport
with the second-highest misconnection rate, was the airport that had the third-lowest
average connection time of 93.55 minutes in 2010. Furthermore, TPA, which had the
lowest misconnection rate in 2010, had the highest average connection time (136.95
minutes) across all the 50 carriers that year.
Our analysis of misconnection rates at the airport level show that 2007 was the year that
had the highest average misconnection rate across the top 50 busiest airports. EWR and
LGA consistently ranked highly in terms of the highest misconnection rates as well as in
arrival delay over the 2006 - 2010 time period. TPA, on the other hand, had consistently
low misconnection rates during the time period and ranked highly in terms of highest
average connection time across the top 50 carriers. Our results suggest that arrival delay
and available connection time play a huge role in affecting the rate of misconnections.
3.2 Misconnections at the Carrier-Specific Level
In this section, we analyze misconnections at the carrier-specific level. As in the analysis
of cancellation rates by carrier, we categorize one-stop passengers based on the carrier
operating the first flight of the itinerary. In this section, we conduct a longitudinal
analysis of misconnection rates across the four groups of carriers during the 2006 - 2010
time period and present the results of this analysis year by year. Figure 3.2.1 below
provides an illustration of the trends in overall average misconnection rates across the
time period. The regional carriers are represented by the green line, the legacy carriers
by the blue line, the non-continental carriers by the purple line, and the low-cost
carriers by the red line.
104
Misconnection Rates by Carrier Group
6.00%
5.00%
4.00%
0
~3.00%
0
S2.00%
-1.00%
Legacies
LCCs
---
0.00%
2006
2007
2008
2009
2010
Regionals
-"-Non-Cons
Year
2006
2007
2008
2009
2010
Legacies
3.31%
3.93%
3.71%
3.16%,
2.95%
LCCs
2.22%
2.44%
2.53%
2.26%
1.99%
Regionals
Non-Continentals
4.71%
5.31%
4.75%
4.99%
4.11%
3.38%
3.29%
3.01%
3.03%
2.31%
Figure 3.2.1: Misconnection rates for the four carrier groups across the 2006 - 2010
time period
Over the entire 2006 - 2010 time period, the regional carriers had the highest
misconnection rate among the four carrier groups, the low-cost carriers had the lowest
misconnection rates, and the legacies and non-continental ranked between the other
two groups. Next, we present plots that show the distribution of misconnection rates
among the four groups of carriers over the 2006 - 2010 time period. We will then
proceed on to discuss the trends in misconnection rates observed across the years.
Similar to our analysis of cancellation rates, the regional carriers are highlighted in blue,
the legacy carriers in green, the low-cost carriers in orange, and the non-continental
carriers in grey. Figure 3.2.2 below presents the data for the 2006 calendar year.
105
Misconnection Rates by Carrier - 2006
b.UU0%
i
5.00%
0
4.00%
Xw 3.00%
C
C
0
- --
--
II~
2.00%
--
1.00%
U
Overall Average
Misconnection Rate
Regionals
0 Low-cost carriers
nnoL
%
M Legacies
>n
3
Carrir
Carrier
M Non-continentals
Figure 3.2.2: Misconnection rates across the carriers in 2006
In 2006, the average misconnection rate across all the carriers was 3.42%. YV had the
highest misconnection rate (5.63%) and AQ had the lowest misconnection rate (1.41%).
In general, the regional carriers had the highest misconnection rates, with the four
carriers having the highest misconnection rates all being regional carriers. In fact, all the
regional carriers had misconnection rates that were above the overall average
misconnection rate across all carriers. In spite of the low-cost carriers being the carrier
group that had the lowest average misconnection rate, B6 had a high misconnection rate
of 3.42% (it ranked 6th in terms of highest misconnection rates among all the carriers)
and was the only low-cost carrier whose misconnection rate was above the average
misconnection rate across all the carriers. On further investigation, we find that of all
carrier groups, the low-cost carriers had the second lowest average percentage of onestop passengers - only 14.21% of the total passengers who flew on itineraries operated
by low-cost carriers were one-stop passengers, compared to 41.04% for the regionals,
27.91% for the legacies, and only 9.82% for the non-continental carriers. Out of all the
passengers who flew on itineraries operated by B6, only 6.06% were one-stop
passengers; B6 had the lowest percentage of connecting passengers across all the
continental carriers. In terms of arrival delay, B6 had the second-highest average arrival
delay of 17.02 minutes, after EV which had an average arrival delay of 18.72 minutes;
the arrival delay for B6 was more than 49.24% higher than the average arrival delay
across all the low-cost carriers (11.40 minutes). However, the average connection time
106
for itineraries operated by B6 in 2006 was 122.91 minutes, which was the fourth-highest
average connection time across all the carriers. Hence, B6's high average arrival delay is
a major contributor to the carrier's high misconnection rate, more so than its connection
time. Furthermore, B6 operates a hub out of JFK, with 89% of B6's connecting
passengers connecting at the airport in 2007. In 2007, JFK had the third highest average
arrival delay (19.70 minutes) across the top 50 busiest airports, and the 7*11 highest
misconnection rate (4.63%). This illustrates the interdependency that exists between
carriers and the airports at which they operate.
Among the legacy carriers, UA had the highest misconnection rate and was the only
legacy carrier whose misconnection rate was higher than the overall average
misconnection rate across all the carriers. Out of all the passengers who flew on
itineraries operated by UA, 26.52% were one-stop passengers. In terms of arrival delays,
UA had an average arrival delay of 15.77 minutes, making it the carrier with the
second-highest average arrival delay among the legacy carriers. CO was the legacy
carrier with the highest average arrival delay of 16.33 minutes. In terms of
misconnection rates, however, CO ranked lower than UA and had a misconnection rate
of 3.18%. This further illustrates that arrival delays explain some but not all
misconnections. In terms of average connection time, UA had a higher connection time
(119.203 minutes) than CO (113.43 minutes). This, again, is counterintuitive since we
would expect CO to have a higher misconnection rate than UA owing to CO's higher
average arrival delay and CO's lower average connection time compared to UA. To
further investigate this, we consider both UA's and CO's hub airports; UA operated a
hub out of ORD and 19.1% of its operations in 2006 were performed at ORD, and
50.44% of connecting passengers served by UA had their connection at ORD. CO's hub
was at IAH and 28.5% of the carrier's operations in 2006 were performed at IAH, and
87.93% of the one-stop passengers it served that year made a connection at IAH. ORD
had the fifth-highest misconnection rate in 2006 (5.00%) and IAH had a much lower
misconnection rate (3.37%). ORD was also the airport with the second-highest average
arrival delay among the top 50 airports, with an average of 20.78 minutes while IAH
had a much lower average arrival delay of 13.00 minutes. Thus, UA has a bulk of its
operations at an airport that has a higher misconnection rate and arrival delays in
comparison to CO which appears to have more advantage in terms of where it operates.
This may be the reason for the UA's higher misconnection rate and further emphasizes
107
the existence of interdependence between carriers and the airports at which they
operate.
Figure 3.2.3 below presents the misconnection rates for all carriers in 2007, in decreasing
order.
Misconnection Rates by Carrier- 2007
7.00%
6.00%
5.00%
4.00%
-
3.00%
0
2.00%
1.00%
n-0
na
-4
Overall Average Misconnection
Rate
1
U
.: , >i i , .M
Regionals
LOW-COSt carriers
zj
E Legacies
Carrier
0 Non-continentals
Figure 3.2.3: Misconnection rates across the carriers in 2007
In 2007, the overall average misconnection rate across all carriers was 3.97%, which was
16% higher than the overall average in 2006 (3.42%). EV had the highest misconnection
rate of 6.64% and AQ had the lowest misconnection rate of 1.47%. Similar to 2006, the
regional carriers had the highest misconnection rates among all carriers in 2007, with six
out of the seven regional carriers having the highest misconnection rates across all the
carriers. In terms of arrival delay, EV had the highest average arrival delay (21.13
minutes) across all the carriers, and AQ had the lowest average arrival delay (4.07
minutes). The overall average misconnection rate across the regional carriers increased
from 4.71% in 2006 to 5.31% in 2007 (more than a 12% increase in misconnection rate).
However, there was hardly any increase in average arrival delay across the group - the
average arrival delay in 2006 was 15.14 minutes and 15.95 minutes in 2007. Hence, the
increase in misconnection rate cannot all be attributed to increase in average arrival
delay for the regional carriers. In addition, the average connection time for all one-stop
itineraries operated by regional carriers remained approximately the same at 113.33
minutes in 2006 and an average of 113.04 minutes in 2007. To further analyze this, we
consider the airports at which a bulk of the regional carriers' operations is performed.
108
Out of all the flights operated by the regional carriers in 2007, the highest percentage
departed from ORD (7.30%) which was the airport that had the fourth-highest arrival
delay (21.50 minutes) in 2007. In 2006, ORD had the second-highest average delay of
20.78 minutes, and 6.72% of all the flights operated by regional carriers departed from
the airport. Moreover, out of all the airports that served as a connection for all one-stop
passengers served by regional carriers in 2007, ORD served the most with 20.29% of all
the one-stop passengers connecting at the airport. This is a contrast with 2006 when
16.91 % of the one-stop passengers served by regional carriers made their connections at
ORD. Furthermore, 9.78% of all flights operated by the regional carriers in 2007
departed from one of the four airports that had the highest cancellation rates in the both
2006 and 2007 (EWR, IAD, LGA, and CLE); in 2006, this percentage was lower - 6.82%
of the flights operated by the regional carriers that year departed from one of EWR,
IAD, LGA, or CLE. These results suggest that the regional carriers' operations were
carried out at airports that were more susceptible to higher misconnection rates, more
so in 2007 than in 2006. Thus, although the overall average arrival delay values and the
overall
average
connection
time
values
for
the
regional
carriers
remained
approximately the same from 2006 to 2007, the overall average misconnection rate for
the carrier group increased and this may be as a result of the carriers operating at
airports that were susceptible to high misconnection rates.
Four out of the six legacy carriers had misconnection rates that were higher than the
overall average misconnection rate across all the carriers. UA had the highest
misconnection rate (4.65%) across the legacy carriers, and had the second-highest
average arrival delay (17.87 minutes) among the legacy carriers, after AA which had an
average arrival delay of 18.55 minutes. The average misconnection rate across the
legacy-carrier group increased from 3.31% in 2006 to 3.93% in 2007 (more than an 18%
increase in misconnection rate) and the average arrival delay for the group increased
from 13.83 minutes in 2006 to 16.28 minutes in 2007 (more than a 17% increase in
average arrival delay). On the other hand, the average connection time for the legacy
carriers remained approximately the same at 116.71 minutes in 2006 and 117.87 minutes
in 2007. Hence, for the legacy-carrier group, the increase in misconnection rate observed
from 2006 to 2007 may be attributed to an increase in average arrival delay, more so
than for the regional carriers.
109
Similar to 2006, B6 was the low-cost carrier that had the highest misconnection rate
(4.83%), and was also the only low-cost carrier whose misconnection rate was above the
overall average misconnection rate across all carriers. The carrier also had the secondhighest average arrival delay (19.91 minutes) across all carriers. Amongst the noncontinental carriers, AS had the highest misconnection rate of 4.30% and was the only
non-continental carrier whose misconnection rate was higher than the overall average
misconnection rate across all carriers.
Figure 3.2.4 below provides the average misconnection rates across all carriers in 2008
in decreasing order.
Misconnection Rates by Carrier - 2008
7.00%
6.00%
.
5.00%
-
.00%
Overall Average Misconnection
Rate
---
C 3.00%
0
0 2.00%
-
N Regionals
1.00%
0.00%
M
-
0>-L
D><M
Low-cost carriers
Legacies
Airport
N Non-continentals
Figure 3.2.4: Misconnection rates across the carriers in 2008
In 2008, the overall average misconnection rate across all the carriers was 3.70% (which
was a 7.3% decrease in overall average misconnection rate from 2007). OH had the
highest misconnection rate (6.08%) across all carriers and AQ had the lowest
misconnection rate (1.09%). Similar to the previous years, the regional carriers had the
highest misconnection
rates, with the first three carriers in terms of highest
misconnection rates all being regional carriers. All
the regional
carriers had
misconnection rates that were higher than the overall average misconnection across all
carriers. Across the regional-carrier group, the average misconnection rate decreased by
12% from 5.31% in 2007 to 4.75% in 2008. In terms of arrival delay, the carrier group's
average arrival delay decreased by 11% from an average of 15.95 minutes in 2007 to an
average of 14.37 minutes in 2008. Furthermore, the average connection time for all one110
stop itineraries operated by regional carriers remained stable between 113.04 minutes in
2007 and 112.71 minutes in 2008. Hence, the decrease in overall average misconnection
rate for the carrier group from 2007 to 2008 can be attributed to the decrease in average
arrival delay of the flights they operated.
For the legacy carriers, UA had the highest misconnection rate of 4.64% across the
group. UA also had the highest average arrival delay (17.27 minutes) across the legacy
carriers and the second-highest average arrival delay across all carriers. AA was the
only other legacy carrier that had a misconnection rate (4.42%) that was above the
overall average misconnection rate across all carriers. In terms of average arrival delay,
AA had the second-highest arrival delay (17.22 minutes) among the legacy carriers and
the third-highest arrival delay across all the carriers. The two carriers' average
connection times remained stable between 2007 and 2008 - the average connection time
for UA was 120 minutes in both 2007 and 2008, and AA's average connection time was
117 minutes in 2007 and 118 minutes in 2008. Note that these values of average
connection times for UA and AA were very similar to the average connection time
across all legacy carriers which was approximately 118 minutes in 2007 and also in 2008.
Hence, UA's and AA's high misconnection rates can be attributed to the carriers' high
average arrival delay. The average misconnection rate across the legacy-carrier group
decreased slightly from 3.93% in 2007 to 3.71% in 2008 (a 5% decrease in misconnection
rate) while the average delay across the group decreased from 16.28 minutes in 2007 to
14.59 minutes in 2008 (a 12% decrease in average arrival delay). Thus, the decrease in
the group's average misconnection rate across the two years may be as a result of the
decrease in average arrival delay among the carriers.
Similar to 2006 and 2007, in 2008 B6 was the low-cost carrier with the highest
misconnection rate (4.76%). This was more than 1.8 times as high as the average
misconnection rate across all the low-cost carriers (2.53%). B6 was also the only low-cost
carrier whose misconnection rate was higher than the overall average misconnection
rate across all carriers. In terms of average arrival delay, B6 had the highest arrival
delay (18.15 minutes) across all carriers, which was more than 1.5 times as high as the
average arrival delay across the low-cost carriers (11.88 minutes). Among the noncontinental carriers, AS had the highest misconnection rate (3.50%) and all noncontinental carriers had misconnection rates that were below the overall average
misconnection rate across all carriers.
111
Figure 3.2.5 below presents misconnection rates across all the carriers in 2009 in
decreasing order.
Misconnection Rates by Carrier- 2009
8.00%
7.00%
5 6.00%
C
5.00%
0
,Overall Average
Misconnection
Rate
E 4.00%
5U 3.00%
I
-
2 2.00%
U
1.00%
U...V
Regionals
0 Low-cost carriers
'
1
M
1
> >
T
(
I
I
I
-
I
Z
O 0WM
ELegacies
N Non-continentals
Carrier
Figure 3.2.5: Misconnection rates across the carriers in 2009
In 2009, the overall average misconnection rate across all the carriers was 3.24%, which
was more than 14% lower than the average misconnection rate in 2008. OH had the
highest misconnection rate (6.78%) and WN had the lowest misconnection rate (1.61%).
Similar to the previous years, the regional carriers had the highest misconnection rate,
with an average misconnection rate of 4.99% across the group. The four carriers with
the highest misconnection rates were all regional carriers and six out of the seven
regional
carriers had misconnection rates
that were higher
than the overall
misconnection rate across all the carriers. The overall average misconnection rate across
the carrier group decreased by about 5.05%, from 4.75% in 2008 to 4.99% in 2009. The
average arrival delay across the carrier group was 12.44 minutes, which was more than
a 15% decrease in arrival delay from 2008. The average connection time for the regional
carrier group remained stable between 121.75 minutes in 2007 and 120.36 minutes in
2008. Therefore, the decrease in the regional carriers' average misconnection rate can be
attributed to a decrease in the carrier group's arrival delay.
For the legacy carriers, AA had the highest misconnection rate (3.52%). UA had the
third-highest misconnection rate across the legacy carriers, unlike in previous years
whereby it had been the legacy carrier with the highest misconnection rate. The carrier's
112
misconnection rate fell by 39% from 4.64% in 2008 to 3.34% in 2009. Interestingly, UA's
average arrival delay decreased from 17.27 minutes in 2008 to 11.22 minutes in 2009 (a
540%0 decrease in average arrival delay) and the carrier's ranking in terms of arrival delay
improved from 2', position in 2008 to 13*11 position in 2009. Hence, the drop in UA's
misconnection rate can be attributed to the decrease in the carrier's average arrival
delay. The overall average delay across all the legacy carriers increased from 11.76
minutes in 2009 to 14.75 minutes in 2010. Hence, it is not surprising that the average
misconnection rate across the group increased from 3.16% in 2008 to 3.71% in 2009.
Among the low-cost carriers, FL had the highest misconnection rate (3.98%) and was
also the low-cost carrier with the highest increase in misconnection rate from the
previous year (FL's misconnection rate increased from 3.40% in 2008 to 3.89% in 2009.
The carrier's arrival delay increased from an average of 12.21 minutes in 2008 to an
average of 14.89 minutes in 2009. FL's average connection time remained approximately
the same, with an average of 120.16 minutes in 2008 and an average of 119.97 minutes in
2009. This means that the increase in FL's misconnection rate can be attributed to the
increase in arrival delays of flights operated by the carrier.
Among the non-continental carriers, HA had the highest misconnection rate at 3.51%.
HA's misconnection rate increased by 29.93% from an average of 2.46% in 2008.
Moreover, the carrier's ranking in terms of highest misconnection rate changed from
18 t" position in 2008 to 101 position in 2009, among the 20 carriers. HA was the carrier
that had the lowest average arrival delay both in 2008 (5.54 minutes) and in 2009 (4.32
minutes) and its average connection time remained approximately the same across both
years - 117.15 minutes in 2008 and 118.47 minutes in 2009. Interestingly, HNL, which is
the airport where HA had 94% of its operations (both arrivals and departures), was the
airport that had the highest increase in misconnection rate from 2008 to 2009 across the
top 50 busiest airports. This is a further indication of the interdependence between
carriers and the airports at which they operate and may be the reason for the increase in
HA's misconnection rate from 2008 to 2009.Of note, Aloha Airlines went bankrupt and
exited Hawaii's inter-island passenger market in 2008. According to a case study of
Aloha's sudden exit, the inter-island passenger volume did not decrease in response to
Aloha's demise; the remaining carriers and in particular, HA, responded by filling the
gaps left by AQ and this may have resulted in an increase in disruptions as the airlines
at HNL attempted to coordinate their schedules (Kawaura, 2011).
113
Figure 3.2.6 below shows the misconnection rates for all carriers in 2010 in decreasing
order.
Misconnection Rates by Carrier - 2010
6.00%
1
5.00%
0
4.00%
cu 3.00%
mmmmmmjmihmYmimi
Overall Average
Misconnection Rate
C
C
u 2.00%
1.00%
U
1.00%
W
> C
0
Regionals
Low-cost carriers
<><
Carrier
U
U
Legacies
Non-continentals
Figure 3.2.6: Misconnection rates across the carriers in 2010
In 2010, the overall average misconnection rate across all the carriers was 3.00%, which
was also the lowest overall misconnection rate over the 2006 - 2010 time period. As in
the previous years, the regional-carrier group had the highest misconnection rate, with
OH having the highest misconnection rate (5.64%) across all carriers. WN had the
lowest misconnection rate (1.61%). The first five carriers in terms of misconnection rates
were all regional carriers, and six out of the seven regional carriers had misconnection
rates that were higher than the overall average misconnection rate across all the carriers.
In terms of average arrival delay, OH had the highest arrival delay amongst the
regional carriers and the second-highest amongst all carriers.
Among the legacy carriers, DL had the highest misconnection rate across the group
(3.69%) and was the only legacy carrier whose misconnection rate was higher than the
overall average misconnection rate across all the carriers. DL's misconnection rate
increased by 5.93% from 3.47% in 2009 to 3.69% in 2010, and this was the only legacy
carrier whose misconnection rate increased (AA's misconnection rate decreased by
16.95%, UA's decreased by 30.16%, US's decreased by 18.61%, and CO's decreased by
17.97%). In terms of average arrival delay, DL had the highest arrival delay (12.51
minutes) amongst the legacy carriers and the fifth-highest across all the carriers.
114
Interestingly, in 2010 the merger between DL and NW was finalized and the two began
reporting as one carrier under the Delta name. It may be that the difficulty in the
coordination of the two carriers' schedules might have resulted in the increase in
misconnection rates for the carrier. In order to further investigate this, we calculate DL's
misconnection rates at NW's hub airports in 2009 and compare them to 2010. We
consider NW's three main hubs - DTW, MEM, and MSP, and provide the results in
Table 3.2.1 below.
Airport DL's
DL's
Misconnection Misconnection
Rate in 2010
Rate in 2009
DTW
2.43%
3.81%
4.05%
MEM
2.72%
MSP
3.58%
3.51%
Table 3.2.1: DL's misconnection rates at NW's hubs
According to Table 3.2.1, DL's misconnection rates at DTW and MEM increased by 57%
and 49% respectively after the merger completion, and remained approximately the
same at MSP. Across the airports at which at least 5% of DL's one-stop passengers made
their connections, DTW and MEM were the two airports at which DL had the highest
increase in misconnection rate from 2009 to 2010. This attests to the possibility of the
increase in DL's average misconnection rate being partly attributed to the complexities
that resulted from the carrier's merger with NW.
Among the low-cost carriers, B6 had the highest misconnection rate at 3.24%, as well as
the highest average arrival delay across all the carriers. B6's average arrival delay (14.58
minutes) was more than 35% higher than average arrival delay across the low-cost
carriers (10.76 minutes), and this may be the reason for the carrier's high misconnection
rate. Among the non-continental carriers, HA had the highest misconnection rate in the
group.
Our analysis of the misconnection rates across carriers has yielded further evidence that
arrival delays significantly affect misconnection rates. Furthermore, we have found that
the interdependence between carriers and the airports at which they operate also affects
the rate of misconnections. We have observed that some carriers experienced changes in
their average misconnection rates across some years and yet, in some cases, the change
in average arrival delay experienced as well as the average connection time available to
115
non-stop passengers did not explain the increase in misconnection rate. Moreover, we
have found that these carriers have a bulk of their operations at airports which are more
susceptible to higher misconnection rates (they have higher average arrival delays
and/or lower average connection times). This means that although a carrier is able to
keep its arrival delays low (or constant) and maintain the average connection times
available to one-stop passengers, it may experience an increase in misconnection rates
owing to the airports at which it operates. This may particularly be the case for carriers
which operate many one-stop itineraries in collaboration with other carriers, that is,
one-stop itineraries on which the second leg is operated by a different flight from the
first one. To further investigate this, we analyze the one-stop itineraries based on the
carrier(s) operating each of the two legs of the trip. We find that across the 2006 - 2010
time period, one-stop itineraries operated by two different carriers (for each leg of the
trip) accounted for an average of 41.73% of the total one-stop itineraries. We refer to
these itineraries as multi-carrieritineraries versus same-carrieritineraries which are the
ones for which both of the itinerary's legs are operated by the same carrier.
Furthermore, for the continental carriers, we investigate what percentage of either of the
multi-carrier and same-carrier itineraries each of the carrier groups operates. Like
before, we categorize multi-carrier itineraries based on the carrier operating the first leg
of the trip. We present the averages across the 2006 - 2010 time period in Table 3.2.2
below.
% Average of Total
Multi-Carrier
Itineraries
47.51%
% Average of Total
Same-Carrier
Itineraries
11.25%
Regional
Carriers
64.66%
49.99%
Legacy
Carriers
22.09%
0.24%
Low-cost
Carriers
2.00%
2.26%
Noncontinental
Carriers
Table 3.2.2: Multi- and same-carrier itineraries across the carrier groups
According to the above results, the regional carriers and the legacy carriers operate
about 97.5% of the multi-carrier itineraries and hence it is not surprising that both of
116
these carrier groups had the highest misconnection rates across the entire 2006 - 2010
time period. We further analyze what share of the itineraries operated by each of the
carrier groups is accounted for by either of the multi-carrier and same-carrier
itineraries, and provide the results in Table 3.2.3 below.
%Average
%Average
accounted for by accounted for by
Multi-Carrier
Same-Carrier
Regional
Carriers
Legacy Carriers
Itineraries
Itineraries
75.16%
24.84%
35.64%
64.36%
LCCs
0.77%
99.23%
Non59.23%
40.77%
continental
Carriers
Table 3.2.3: Percentage of multi- and same-carrier itineraries across the carrier groups
Multi-carrier itineraries make up 75.16% of the total one-stop itineraries operated by
regional carriers (one-stop itineraries for which the first leg of the trip is operated by a
regional carrier), and same-carrier itineraries make up only 24.84% of the itineraries
operated by the group. This implies that regional carriers operate itineraries that are
more exposed to other carriers' operations and hence more susceptible to passenger
misconnections. The multi-carrier itineraries operated by low-cost carriers make up less
than 1% of the total one-stop itineraries operated by the carrier-group. This implies that
the one-stop itineraries operated by any of the low-cost carriers are likely operated by
the same carrier for the second leg of the trip and hence less exposed to other carriers'
operations and thus less susceptible to misconnections. However, we must note that
regional carriers primarily operate under codeshare agreements with legacy carriers,
that is, the multi-carrier itineraries for each regional carrier are more than likely
operated in conjunction with the same legacy carrier(s). Hence, the adverse effect of
multi-carrier itineraries on misconnection rates for the regional carriers is tempered as
compared to other carriers which may operate in conjunction with many different
carriers.
117
3.3 Schedule Banking
After deregulation and up until the early 2000s, most hub-and-spoke carriers in the
United States operated banked (or peaked) schedules at their hub airports. In a banked
schedule, flights arrive in a wave (called an arrival bank) and, soon afterward, depart
from the hub in another wave (called a departure bank). This makes short connections
available for passengers, allowing them to quickly connect between a flight in the
arrival bank and a flight in the subsequent departure bank. This aspect of operating
banked schedules allows an operating carrier to compete against carriers that offer nonstop service or service through other hubs (Katz, 2012). Banked schedules, however, are
an inefficient use of airline resources - between the peak activity periods, gates,
equipment, and personnel remain unutilized and sit idle. De-banking (or de-peaking)
an airline's schedule helps solve this issue of under-utilization of resources and allows
an airline to be more efficient thus reducing the cost to operate the schedule. Hence,
since the early 2000s, several major carriers in the U.S. adopted this technique of
operating a de-banked schedule at some of their hubs. AA was the first airline to debank its hubs at both ORD and DFW in 2002, in response to the economic downturn
after the 9/11 terrorist attacks (Reed, 2006). In 2005, DL, US, and UA followed suit by
de-banking their hubs at ATL, PHL, and LAX respectively, and in 2006 UA also debanked its SFO hub.
Figure 3.3.1 below is an example of a banked schedule. It represents a distribution of
NW's operations at MEM from 7:00am to 10:00pm in 2007.
118
Distribution of NW's Operations at MEM
in 2007
7000
6000
5000
4000
.0
* arrivals
00
Q3000
E
5 2000
" departures
z
1000
0
7
8
9
10
11
12 13 14
Hour of day
15
16
17
18
19
20
21
Figure 3.3.1: NW's banked schedule at MEM in 2007
One can easily identify three distinct banks in NW's schedule at MEM in 2007. In
contrast, AA's schedule at ORD during the same year represents a de-banked schedule,
as shown in Figure 3.3.2 below.
Distribution of AA's operations at ORD in
2007
8000
7000
.~6000
S5000
4000
"-O
* departures
.3000
E
S2000
* arrivals
1000
0
7
8
9
10
11
12
13 14 15
Hour of day
16
17
18
19
20
21
Figure 3.3.2: AA's de-banked schedule at ORD in 2007
As can be observed above, a de-banked schedule is described as a continuous schedule
due to the consistent level of operations which occurs throughout the day at the airport.
119
In comparison to the banked schedule, the distribution of operations for a de-banked
schedule is more uniform and flatter.
To measure the extent of banked operations by a carrier at an airport, we employ a
metric developed by Jenkins, Marks, and Miller (Jenkins et al., 2012) referred to as the
peak index. The peak index for a carrier at an airport is defined as the coefficient of
variation of one-hour periods of activity throughout the day (i.e., the ratio of the
standard deviation to the mean of the number of operations per hour) for that carrier at
that airport, Which we provide as a percentage. A higher value of the peak index
represents a greater extent of banked operations, and vice versa. For instance, the peak
index associated with NW's operations at MEM in 2007 is 92.40% while that for AA's
operations at ORD that year was 15.72%.
In addition, we provide Figures 3.3.3 and 3.3.4 that compare DL's operations at its ATL
and JFK hubs in 2007.
Distribution of DL's operations at JFK in
2007
g
W
-o
E
z
4500
4000
3500
3000
2500
2000
1500
1000
500
0
" arrivals
" departures
Li
7
L
8
9
10
11
12
13
14
iti
15
16
Hour of day
Figure 3.3.3: DL's banked schedule at JFK in 2007
120
17
18
19
20
21
Distribution of DL's operations at ATL in
2007
16000
14000
S12000
'~10000
0
S8000
E
S6000
LU HUE EU U HUE HUE
-u
S4000
arrivals
* departures
2000
0
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Hour of day
Figure 3.3.4: DL's de-banked schedule at ATL in 2007
As is clear from Figures 3.3.3 and 3.3.4 above, DL operated two different types of
schedules at its JFK and ATL hubs
a banked schedule at JFK and a de-banked
schedule at ATL. The peak index associated with DL's banked schedule at JFK in 2007 is
78.20% and that for the carrier's de-banked schedule at ATL is 13.65%.
Now, as mentioned earlier, one characteristic of banked schedules is that they provide
short passenger connection times. De-banking, therefore, results in longer average
connection times for passengers (Katz, 2012). This is evidenced in the average
connection times obtained at each of the above cases as shown in Table 3.3.1 below.
Carrier
Airport Peak
Index
Value
Average
Connection
Time
(Minutes)
NW
MEM
92.40% 84.08
DL
JFK
78.20% 116.76
AA
ORD
15.72% 122.46
DL
ATL
13.65% 122.94
Table 3.3.1: Peak index values and connection times for the NW, AA and DL
examples
121
The average connection times for both NW's operations at MEM and DL's operations at
JFK are higher than the average connection times for the other two cases. The peak
index value associated with NW's banked schedule at MEM is the highest among the
four cases, and the average connection time of NW at MEM is 84.08 minutes which is
the shortest average connection time among the four cases.
We will use the peak index values to investigate the effect of banked schedules on
misconnection rates at different airports. We consider five airports which consistently
ranked among the top 10 in terms of highest misconnection rates across the 2006 - 2010
time period. These airports are EWR, JFK, LGA, IAD, and SFO. For these airports, we
calculate the average misconnection rate values, the average arrival delay values, and
the average connection time across the 2006 - 2010 time period. The results are
provided in Table 3.3.2 below in order of decreasing average misconnection rate.
Airport Average
Misconnection
Rate
Average
Flight
Arrival
Delay
(minutes)
EWR
6.26%
24.04
LGA
6.17%
18.68
SFO
5.20%
17.58
IAD
5.15%
13.61
JFK
4.78%
19.13
Table 3.3.2: Average flight delay and average connection
consistently high misconnection
Average
Connection
Time
(minutes)
119.55
105.22
123.54
100.66
124.33
time at the five airports with
rates
According to the results in Table 3.3.2 above, JFK has the lowest average misconnection
rate among the five airports but its average arrival delay across the time period is the
second-highest, ranking above SFO, IAD, and LGA, all of which have higher average
misconnection rates. In terms of average connection time, however, JFK has the longest
average connection time, resulting in the airport's low relative misconnection rate.
Owing to the fact that our research is dependent on passenger itineraries that are
obtained from multinomial logit model estimation, we need to address the question of
whether the differences observed in connection times at the airports are simply a
construct of the passenger itineraries or whether they indicate something more
122
fundamental about the schedule structure at these airports. To address this question, we
use the peak index to further investigate the banking structure at two of the above
airports - JFK and IAD. For these two airports and for all carriers that serve at least 10%
of the airport's connecting passengers, we calculate the average peak index and the
average connection times across the 2006 - 2010 time period. The results are provided in
Table 3.3.3 below.
Airport
Carrier
Average % Average
of Airport's Peak Index
Connecting
Passengers
Average
Connection
Time
(minutes)
IAD
UA
49.17%
50.23%
104.58
IAD
YV
51.95%
55.75%
96.46
JFK
B6
86.45%
24.12%
123.34
Table 3.3.3: Average peak index values, average connection time, and average share
of connecting passengers at IAD and JFK
According to Table 3.3.3 above, the average peak index of both UA and YV at IAD has
average peak index values that are more than double the average peak index of B6 at
JFK, which results in much shorter average connection times at IAD for both UA and
YV as compared to B6's average connection time at JFK. These results suggest that the
shorter average
connection times observed at IAD (and consequently higher
misconnection rate) are a result of the banked schedules operated by carriers at the
airport as opposed to being an artifact of the passenger itinerary flow estimation
procedure.
3.4 Modelling Missed Connections
In this section, just like we did with cancellations, we present regression models that
explain the observed variability in misconnections. For each year within the 2006 - 2010
time period, we aggregate the individual passenger itineraries into carrier-airport-day
combinations, that is, each combination of carrier, airport and day represents a single
observation, whereby carrier refers to the carrier operating the first flight of the
itinerary. In order to eliminate issues related to sample size, we choose to consider only
the carrier-day-airport combinations that have at least 100 one-stop passengers.
123
The dependent variable for our models is the average misconnection rate for the nonstop passengers associated with each observation. For each year, we present results
from three regression models, with the third model building on the results from the
second model and the second model building on the results from the first model. This
incremental approach, which we also employed previously to model cancellations,
allows us to capture the relative impact of the inclusion of the variables. For each of the
explanatory variables, we calculate the average of the appropriate value across the onestop passengers corresponding to the observation. We weight each of the observations
based on the number of connecting passengers corresponding to each carrier-airportday combination.
Based on our discussion earlier, average flight delays at an airport largely affect the rate
of misconnections observed at the airport. Hence, for our first regression model, we
attempt to predict the misconnection rates using the average flight delays as the only
explanatory variable, along with an intercept. As also discussed, schedule banking and
connection times also have an impact on passenger misconnection rates sincc longer
connection times result in a lower risk of misconnections. Hence, in our second
regression model, we include airport average connection times as an additional
explanatory variable. Furthermore, we established that different carrier groups vary in
terms of misconnection rates across the 2006 - 2010 time period - among the continental
carriers, the regionals had the highest misconnection rates, followed by the legacy
carriers, and the low-cost carriers had the lowest misconnection rates. Therefore, for our
third regression model we include two 0-1 carrier dummy variables - one for the
regional carriers and the other for the low-cost carriers. These two dummy variables
implicitly measure the scheduling practices of these two carrier groups with those of the
legacy carriers. We choose to eliminate the non-continental carriers because they served
an average of less than 2% of the total connecting passengers across the 2006 - 2010 time
period. For each year within the period, we present and discuss the results obtained
from the three regression models. Table 3.4.1 below shows the estimation results from
the three models for 2006.
124
Model
Parameter Description
Estimate
Std
Error
PValue
Adjusted
R-Square
Value
9.32E0.00
04
6.79E0.00
2.71E-03
1
Average Flight Delay (minutes)
05
3.49E0.00
Intercept
6.50E-02
2
03
6.80E0.00
Average Flight Delay (minutes) 2.85E-03
2
05
0.00
Time -5.57E-04 2.93EConnection
2
Average
05
(minutes)
0.00
3.61E2.76E-02
Intercept
3
03
0.00
6.96E2.23E-03
Average Flight Delay (minutes)
3
05
0.00
Time -1.63E-04 3.13EConnection
Average
3
05
(minutes)
0.00
6.53E1.35E-02
Regional Carrier Dummy
3
04
0.00
-1.31E-02 6.28ELow-cost Carrier Dummy
3
04
Table 3.4.1: Estimation results for the regression models for 2006
1
Intercept
1.06E-03
0.4052
0.4959
0.5179
As expected, in all three models, the estimated coefficient of average delay is positive
and statistically highly significant with at least a 99% confidence level, which implies
that an increase in average flight delay results in a higher passenger misconnection rate.
The adjusted R-squared value from the first regression model implies that average
flight delays explained about 40.52% of the variation in misconnection rates across our
observations. The estimated coefficients of the average connection time in both the
second and third regression models are negative and statistically significant at the 99%
confidence level, which implies that higher connection times result in fewer
misconnections. According to the adjusted R-squared value from the second model,
average passenger connection time values help explain an additional 9% of the
variation in misconnection rates. According to the results from the third model, the
estimated coefficient associated with the regional carrier dummy is positive while that
associated with the low-cost carrier dummy is negative. Both these estimates are highly
125
significant with at least a 99% confidence level. The results suggest that the regional
carriers had the highest misconnection rates among the three carrier-groups, and the
low-cost carriers had the lowest misconnection rates, which is consistent with our
earlier results. The inclusion of the carrier-type variables helps explain another 2.2% of
the variation observed in misconnection rates in 2006.
In Table 3.4.2 below, we provide the estimation results for 2007.
Model
Parameter Description
Estimate
1
Intercept
2.46E-03
1
Average Flight Delay (minutes)
2.71E-03
2
Intercept
7.85E-02
2
Average Flight Delay (minutes)
2.84E-03
2
-6.62E-04
3
Average
(minutes)
Intercept
3
Average Flight Delay (minutes)
2.15E-03
3
Average
-2.81E-04
Connection
Time
4.56E-02
Connection
Time
(minutes)
3
Regional Carrier Dummy
Std
Error
9.32E04
6.15E05
3.29E03
6.13E05
2.75E05
3.35E03
6.28E05
2.88E-
PValue
Adjusted
R-
0.00
Squared
Value
0.4723
0.00
0.00
0.4861
0.00
0.00
0.00
0.4952
0.00
0.00
05
1.10E-02
6.22E0.00
04
-1.66E-02 6.29E0.00
3
Low-cost Carrier Dummy
04
Table 3.4.2: Estimation results for the regression models for 2007
According to Table 3.4.2 above, the estimated coefficients for the average flight delay,
average connection time, the regional carrier dummy, and the low-cost dummy all have
the expected signs (positive for average flight delay and the regional carrier dummy
and negative for the average connection time and the low-cost carrier dummy).
Furthermore, all the coefficients are statistically significant at the 99% confidence level.
126
According to the adjusted R-squared values obtained for the first model, average flight
delays in 2007 helped explain about 47.23% of the variation in misconnection rates,
which is about 7% higher than in 2006. Average connection times explained an
additional 1% of the variation in misconnection rates that was observed in 2007, and the
inclusion of the carrier-type variables helped explain another additional 1/0 of the
variation in misconnection rates that was observed in 2007. In comparison to 2006,
average flight delays had a more significant impact on misconnection rates in 2007. That
is to say, with average flight delays higher in 2007, higher misconnection rates that were
observed were mostly as a result of this higher delay. In addition, the relative impact of
the inclusion of the connection time variable and the carrier-type variables was lower in
2007 than in 2006.
In Table 3.4.3 below, we present the estimation results for 2008.
Model
Estimate
Parameter Description
Std
Error
PValue
Adjusted
RSquared
Value
1
Intercept
5.78E-03
1
Average Flight Delay (minutes)
3.50E-03
1.55E03
1.09E-
0.00
0.4262
0.00
04
2
Intercept
6.83E-02
2
Average Flight Delay (minutes)
3.73E-03
2
3
Average
(minutes)
Intercept
3
Average Flight Delay (minutes)
Connection
Time
-6.54E-04
3.41E-02
2.99E-03
4.90E03
1.10E04
4.10E05
5.13E03
1.16E-
0.00
0.00
0.00
0.00
0.00
04
3
Average
Connection
Time
-2.81E-04
4.45E-
0.00
05
(minutes)
3
Regional Carrier Dummy
1.34E-02
3
Low-cost Carrier Dummy
-1.17E-02
127
1.OOE03
1.03E03
0.4326
0.00
0.00
0.4413
Table 3.4.3: Estimation results for the regression models for 2008
Similar to 2006 and 2007, all the estimated coefficients for the explanatory variables
have the expected signs and are statistically significant at the 99% level. In 2008, the
inclusion of average flight delays as the only explanatory variable helped explain about
42.62% of the observed variability in misconnection rates that year. The inclusion of
average connection time helped explain an additional 1% of the variability in
misconnection rates, and the inclusion of the carrier-type variables also helped explain
another 1% of the variability in misconnection rates that year. The average flight delay
across all the airports in 2008 was 12.68 minutes, which was lower than the average
flight delay in both 2006 and 2007.
In Table 3.4.4 below, we provide the estimation results for 2009.
Model
Parameter Description
Estimate
Std
Error
PValue
Adjusted
Rsquared
Value
1
Intercept
1.82E-04
1
Average Flight Delay (minutes)
3.19E-03
2
Intercept
8.11E-02
2
Average Flight Delay (minutes)
3.45E-03
2
Average
-7.11E-04
Connection
Time
(minutes)
2.10E03
1.82E04
6.78E03
1.82E04
5.67E-
0.00
0.00
0.00
0.4812
0.00
0.00
05
3
Intercept
4.99E-02
3
Average Flight Delay (minutes)
2.69E-03
3
Average
Connection
Time
-3.89E-04
7.13E03
1.92E04
6.16E-
0.00
0.00
0.00
05
(minutes)
0.00
1.42E03
0.00
-7.73E-03 1.43ELow-cost Carrier Dummy
3
03
Table 3.4.4: Estimation results for the regression models for 2009
3
0.4782
Regional Carrier Dummy
1.46E-02
128
0.5166
Similar to the previous years, in 2009, the estimated coefficients for the explanatory
variables all had the expected signs and were statistically significant at the 99%
confidence level. Average flight delay helped explain about 47.82% of the variability
observed in misconnection rates that year. Inclusion of average connection times as an
explanatory variable for misconnections helped explain only a further 0.3% of the
variability observed in misconnection rates in 2009, and the inclusion of the carrier-type
variable helped explain a further 3.54% of the variability observed in misconnection
rates. This implies that the average connection times were not as big a factor in affecting
misconnections compared to the other variables in 2009 as in previous years.
In Table 3.4.5 below, we present the estimation results for 2010.
Model
Parameter Description
Estimate
1
Intercept
1.21E-02
Std
Error
P-Value
1.25E0.00
03
1
Average Flight Delay (minutes) 4.21E-03
1.13E0.00
04
2
Intercept
5.58E-02
2.59E0.00
03
2
Average Flight Delay (minutes) 4.71E-03
1.13E0.00
04
2
Average
Connection
Time -6.27E-04 2.11E0.00
(minutes)
05
3
Intercept
3.07E-02 2.68E0.00
03
3
Average Flight Delay (minutes) 3.49E-03
1.18E0.00
04
3
Average
Connection
Time -3.16E-04 2.30E0.00
(minutes)
05
3
Regional Carrier Dummy
1.34E-02
6.01E0.00
04
3
Low-cost Carrier Dummy
-7.90E-03 5.95E0.00
04
Table 3.4.5: Estimation results for the regression models for 2010
129
Adjusted
Rsquared
Value
0.4330
0.4536
0.4775
In 2010, all the explanatory variables had the expected signs and were significant with
at least 99% confidence level. The average flight delays explain 43.30% of the variability
observed in misconnection rates that year and the inclusion of the average connection
time variable helped explain an additional 2% of the variability observed in
misconnection rates. The inclusion of the carrier group dummies in the third model also
helped explain an additional 2% of the variability observed in misconnection rates that
year.
According to the results of our analysis in this section, we have established that for all
the years during the 2006 - 2010 time period, average flight delays had the most
significant impact on misconnections. Average connection times as well as the carrier
type also had an impact on misconnection rates, but to a much lesser degree as
compared to the average flight delays.
130
Chapter 4
Conclusions and Further Research
As discussed in Chapter 1, prior studies have shown that passengers bear the largest
portion of delay costs in the U.S. air transportation system (Ball et al., 2010).
Furthermore, it has been estimated that about 50% of the total delays suffered by air
travelers are caused by travel disruptions and are borne by only about 3.3% of air
travelers in the U.S (Barnhart, Fearing, and Vaze, 2013). Thus, it is very important to
develop a better understanding of the underlying factors that influence passenger travel
disruptions and develop mechanisms to mitigate them. We believe that the work
presented in this thesis is a step in this direction.
In this thesis, we applied data analysis and statistical modeling techniques to historical
flight and passenger data in order to improve our understanding of the patterns in, and
causes of, passenger travel disruptions in the U.S. We conducted a longitudinal
analysis over the 2006 - 2010 time period in order to gain insights into the trends within
the aviation industry and how they affected travel disruptions. Our results suggest that
there exist significant variations in the propensity for disruptions across airports and
across carriers. This is a result of differences in scheduling practices, network structures,
and passenger connections. In Chapter 2, we analyzed cancellations, which are the
disruptions that cause the most delay to passengers. We present the cancellation rates at
the top 50 busiest airports and for the 20 major carriers in the U.S., over the five-year
period. We illustrate the interdependence that exists between airports and carriers, and
separate out the airport-specific factors from the carrier-specific ones using a metric
called carrier effect. Our results suggest that cancellations vary substantially across
carriers, even when accounting for baseline variability across the airports. We identify
flight frequency and load factors as two factors that significantly impact the airlines'
decision-making process regarding cancellations and investigate how differently these
two factors impact different carrier types. Our findings suggest that for regional
carriers, the decision-making process regarding whether or not to cancel a flight on a
particular carrier-segment is impacted more by the average load factor on the carriersegment than by average flight frequency.
In Chapter 3, we analyze misconnections that, after cancellations, are the disruptions
that result in the second-highest delay to passengers. We present the misconnection
131
rates at the 50 busiest airports and for the 20 major carriers, across the five years.
According to our findings, much of the variability observed in misconnection rates
across airports and carriers can be explained by arrival flight delays, but the type of
flight schedule is also a significant factor. Highly banked (or peaked) flight schedules
result in shorter connection times for passengers and therefore increase the risk of
missed connections. In addition, we found evidence suggesting that serving many
multi-carrier one-stop itineraries might make a carrier more susceptible to higher
misconnection rates.
The longitudinal nature of this analysis allows us to gain insights into the effects of
significant events in the aviation industry. An example of such an event is the
Valentine's Day storm in 2007 that heavily impacted operations particularly at JFK and
particularly for JetBlue which operates a hub at the airport, resulting in higher
cancellation rates and misconnection rates that year. Another example is the Aloha
Airline's bankruptcy and sudden exit from Hawaii's inter-island passenger market in
2008 that resulted in higher misconnection rates at Honolulu International Airport
(HNL) and for Hawaiian Airlines (HA) due to complexities involved in meeting the
increased need for supply (Kawaura, 2011). Yet another example is the 2010 merger
between Delta and Northwest when the two carriers began operating under the Delta
name. We found that both cancellation rates and misconnection rates increased for
Delta and at the carrier's hub in Atlanta, presumably due to the complexities that
resulted from coordinating the two carriers' operations.
Our analysis provides an important first step towards proposing methods that will help
in mitigating passenger travel disruptions in the future through more informed
decision-making. The insights gained from the impact of airline scheduling practices
and network structures will allow airlines to design their networks and schedules in
ways that reduce passenger disruptions. Understanding the impact of passenger
itinerary choices on the likelihood of disruptions will enable passengers to make better
itinerary choices. By understanding the bottlenecks in the system that result in major
disruptions, industry regulators can make better investments in air transport
infrastructure and institute laws that mitigate passenger delays.
The analysis done in this thesis spans a period of five years. However, with the recent
changes in the industry, including three significant mergers (United Airlines and
Continental Airlines in 2010, Southwest Airlines and AirTran Airways in 2011, and US
132
Airways and American Airlines in 2013), evolving hub structures, and recent regulatory
changes, this analysis should be applied over the recent years in order to better
understand the impacts of the operational changes. Another area for future research is
to extend the work done by Barnhart, Fearing and Vaze (2013) to quantify the cost to
passengers of cancellations and missed connections. By quantifying passenger delays
and associated costs in more recent years, the impact of recent trends in the aviation
industry could be understood, and these insights could be utilized in developing future
scheduling practices and policies that mitigate the effects of congestion in aviation
systems.
133
References
[1]
[2]
Airlines for America. Annual and per-minute cost of delays to U.S. airlines, 2013:
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airport-technology.com. Chicago O'Hare International Airport (ORD/KORD),
United States of America, 2013: http://www.airporttechnology.com/projects/chicago/ ( accessed May 12, 2014).
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Ball, M., Barnhart, C., Drener, M., Hansen, M., Neels, K., Odoni, A., Peterson, E.,
Sherry, L., Trani, A., Zou, B., et al. Total delay impact study: a comprehensive
assessment of the costs and impacts of flight delay in the United States. NEXTOR
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[4]
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[5]
[6]
Brennan, J., Morgan, F. JetBlue: high-flying airline melts down in ice storm, 2007.
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J., Miller,
M. The state of U.S. aviation: comprehensive
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135
Appendix A
Abbreviations
IATA Code
9E
AA
AQ
AS
B6
CO
DL
EV
F9
FL
HA
MQ
NW
OH
00
UA
US
WN
XE
YV
Table A.1:
Carrier Name
Pinnacle Airlines 2
American Airlines
Aloha Airlines
Alaska Airlines
JetBlue Airways
Continental Airlines
Delta Airlines
Atlantic Southeast Airlines 3
Frontier Airlines
Air Tran Airways
Hawaiian Airlines
American Eagle Airlines 4
Northwest Airlines
Comair
Sky West Airlines
United Airlines
US Airways
Southwest Airlines
ExpressJet Airlines
Mesa Airlines
Carrier names and IATA codes
2 Now
known as Endeavor Air since May 1, 2013.
' Was re-branded to ExpressJet in November, 2011.
4Now known as Envoy Inc.
136
IATA
Code
ABQ
ATL
AUS
BNA
Airport Name
Albuquerque International Sunport
Hartsfield-Jackson Atlanta
International
Austin-Bergstrom International
Nashville International
IATA
Code
LGA
MCI
New York LaGuardia
Kansas City International
MCO
MDW
Orlando International
Chicago Midway
Airport Name
International
BOS
BWI
CLE
Boston Logan International
Baltimore Washington International
Cleveland Hopkins International
MEM
MIA
MSP
Memphis International
Miami International
Minneapolis - St. Paul
International
CLT
CMH
CVG
Charlotte Douglas International
Columbus Regional
Cincinnati/Northern Kentucky
OAK
ONT
ORD
Oakland International
Ontario International
Chicago O'Hare International
PDX
PHL
PHX
Portland International
Philadelphia International
Phoenix Sky Harbor
International
DAL
DCA
DEN
Dallas Love Field
Reagan National
Denver International
International
DFW
DTW
Dallas/Fort Worth International
Detroit Metro
PIT
RDU
Pittsburgh International
Raleigh-Durham
International
EWR
FLL
SAN
SAT
San Diego International
San Antonio International
HNL
Newark Liberty International
Fort Lauderdale - Hollywood
International
Honolulu International
SEA
HOU
IAD
IAH
IND
JFK
LAS
Houston Hobby
Washington Dulles International
Houston George Bush
Indianapolis International
John F Kennedy International
Las Vegas - McCarran International
SFO
SJC
SLC
SMF
SNA
STL
Seattle - Tacoma
International
San Francisco International
Mineta San Jose International
Salt Lake City International
Sacramento International
John Wayne
Lambert-St. Louis
International
Tampa International
LAX
Los Angeles International
Table A.2: Airport names and IATA codes
137
TPA