O’Hare Airport: An Investigation of the Relationship
between Airline and Road Traffic
By
Dustin Parker
An Undergraduate Thesis
Submitted in Partial Fulfillment for the Requirements of
Bachelor of Arts
in
Geography and Earth Science
Advised by Dr. Wenjie Sun
Carthage College
Kenosha, WI
April, 2013
Abstract
Geography Information Systems (GIS) is used highly in transportation in order to
improve traffic flow for the future. Chicago O’Hare is one of the busiest airports in the world.
Thousands of airlines fly in and out of the airport monthly. With so many arrivals and
departures of airlines comes an ample supply of automotive traffic. Often there are traffic jams
in Chicago and near O’Hare. Some of these traffic jams might be related to airline traffic at
O’Hare airport. There might be a correlation between the number of airline arrivals and
departures with the traffic count near the airport. There could be a time lag in the correlation
as well. The time lag might depend on if the flight is arriving or departing. The transportation
industry could benefit from information like this. If there is a correlation it would be possible to
determine how much traffic would be traveling through due to the airport. This would make
estimating appropriate highway sizes easier for locations near airports.
2
Table of Contents
Table of Contents
Abstract ........................................................................................................................................................ 2
List of Figures ................................................................................................................................................ 4
List of Tables ................................................................................................................................................. 4
Introduction and Literature Review ............................................................................................................ 5
Problem Statement .................................................................................................................................... 10
Methodology .............................................................................................................................................. 11
Data Acquisition ...................................................................................................................................... 11
Correlation Analysis................................................................................................................................. 13
Traffic Count Sample Points .................................................................................................................... 14
Results......................................................................................................................................................... 18
Airline Departures and Traffic Count Comparison .................................................................................. 18
Airline Arrivals and Traffic Count Comparison ........................................................................................ 22
Correlation Analysis ................................................................................................................................ 26
Airline Departures and Short Distance Traffic .................................................................................... 26
Airline Departures and Long Distance Traffic ..................................................................................... 27
Airline Arrivals and Short Distance Traffic .......................................................................................... 28
Airline Arrivals and Long Distance Traffic ........................................................................................... 29
Conclusion .................................................................................................................................................. 30
Discussion................................................................................................................................................ 30
Outlook and Future Direction ................................................................................................................. 34
Acknowledgments ...................................................................................................................................... 36
Appendix..................................................................................................................................................... 36
References .................................................................................................................................................. 41
3
List of Figures
Figure 1 - Location of all 6 sample points ................................................................................................... 15
Figure 1 - Location of all 6 sample points ................................................................................................... 15
Figure 1 - Location of all 6 sample points ................................................................................................... 15
Figure 1 - Location of all 6 sample points ................................................................................................... 15
Figure 1 - Location of all 6 sample points ................................................................................................... 15
Figure 1 - Location of all 6 sample points ................................................................................................... 15
Figure 1 - Location of all 6 sample points ................................................................................................... 16
Figure 1 - Location of all 6 sample points ................................................................................................... 16
Figure 1 - Location of all 6 sample points ................................................................................................... 16
Figure 1 - Location of all 6 sample points ................................................................................................... 16
Figure 1 - Location of all 6 sample points ................................................................................................... 16
Figure 1 - Location of all 6 sample points ................................................................................................... 16
Figure 1 - Location of all 6 sample points ................................................................................................... 16
List of Tables
Table 1 - Airline Departures and Short Distance Traffic Correlations......................................................... 27
Table 2 - Airline Departures and Long Distance Traffic Correlations .......................................................... 28
Table 3 - Airline Arrivals and Short Distance Traffic Correlations ............................................................... 29
Table 4 - Airline Arrivals and Long Distance Traffic Correlations ................................................................ 30
4
Introduction and Literature Review
The goal of this thesis is to look at the correlation between traffic levels on highways at
varying distances from Chicago O’Hare airport and numbers of airline arrivals and departures at
O’Hare International Airport. Hourly highway traffic data will be taken along with hourly airline
arrivals and departures for a total of six days worth of data. Multiple methods will be used to
obtain an accurate result. Not many articles or research have been published attempting to
find a correlation between traffic levels and airline arrival and departure volumes at airports.
Currently there are very few if any books that concentrate exclusively on geographic
information systems and transportation (GIS-T) (Goodchild, 2000). However, the methods that
will be used to find this correlation in my thesis have been used extensively. The literature
review will cover similar methods that will be needed to properly analyze the data for this
thesis. Some articles relating to transportation geography provide important insights. Different
trends of uses for transportation geography include using traffic data for statistical purposes,
finding specific challenges of the subject and how to combat them in the future. By looking at
these works of literature on the subject it is possible to see specific methods that could be used
to find a correlation between road and airline traffic and in some cases how to improve them.
5
Chicago O’Hare was named in 1945 after Lieutenant Commander Edward O’Hare who
was from Chicago, Illinois and fought in World War II. It wasn’t until 1955 that O’Hare official
opened for commercial flights. The airport grew very quickly to become one of the busiest in
the world. By around 1970 there were about 40 million travelers passing through the airport a
year. Currently Chicago O’Hare is still one of the busiest airports in the world and the second
busiest in the United States (Lewis, 2011).
It is hard to determine when exactly the term geographic information systems was first
used, but GIS-based ideas emerged in the early 1960s. Goodchild (2000, Pg 1) explains that a
group of graduate students in Quantitative Geography at the University of Washington in the
late 1950s had a major influence on the start of GIS. One student, Duane Marble, possibly had
the largest impact. She created a form of GIS in order to study transportation in Chicago.
Although this form of GIS she made was basic compared to current forms, it can be considered
one of the first times GIS was used.
Using GIS-T and analyzing data from it requires many different techniques and
approaches. Goodchild explains how current forms of GIS-T consist of three different “views”
including “the map view,” “the navigational view,” and “the behavioral view,” (Goodchild, Pg 2).
The map view uses two dimensional views, often converting them from one dimension. An
example would be a street address that needs to be converted from one dimension to two
dimensional coordinates so it can be placed on a map (Goodchild, pg 2). In the 1970s a system
of links and nodes was used to represent the street network. Although this system was easy to
check for mistakes (since the links must be closed) it had multiple disadvantages. One was that
6
it was difficult to create intersections. Goodchild explains how the map view can be limited
since real features need to be represented as one dimensional spaces or centerlines
(Goodchild, pg 3). The navigation view is based off the “link / node system”. The “link / node
system” is essential to the navigation view since navigation requires following a set path
(Goodchild, pg 4). This thesis will use all three views. The behavioral view deals with only
discrete objects such as vehicles, airplanes, and trains. This view will obviously help when
analyzing airline and road traffic in this thesis. The navigation view will also be used since traffic
flow to and from Chicago O’Hare follows certain path just like a link / node system. The final
product of this thesis will use a map view to display the data.
Transportation will keep growing in the future; therefore it is important to find
improved methods for the future. Rodrigue explains that growing demand from increasing
population, reduction of costs as technology improves, and expansion of infrastructures will
cause the importance of transportation to grow. GIS-T will be very useful in helping to find
these methods. By finding a correlation between air and road traffic this will allow better
judgment regarding how to design highways near Chicago O’Hare and other airports for
improved traffic flow. This information could be very useful for highway designers and the
future of the transportation system. GIS-T research and data is currently fairly scarce, but will
continue to grow in the future according to many researchers such as Rodrigue and Goodchild
have explained.
Airline departure and arrival times at airports can have patterns by themselves. In order
to see a correlation between airline and automotive traffic it is important to take into account
7
the fluctuations airline traffic can have alone. Li Shan-Mei (2012) looks at only airline arrival
and departure traffic for multiple United States airports and airlines. This article is very helpful
since it provides information on how airline arrivals and departure patterns at Chicago O’Hare
could look like and potentially what can be expected when recording data. Li Shan-Mei found
that there are a number of reasons for airline fluctuations at certain airports (Li Shan-Mei, Pg
1). The time scale that is used to measure data has a significant effect. At earlier hours in the
day after midnight and 0600 there is little airline traffic. This means that the data collected in
this time range might skew the results if only these times are used. Environmental influences
can have a major effect on airline traffic as well. Li Shan-Mei explains how severe and nonsevere weather can influence airline traffic flow patterns (Li Shan-Mei, Pg 5). In order to
accurately measure the correlation of airline and automotive traffic the time scale and
environmental issues will have to be taken into account.
Lewis (2011) also covers only airline traffic. Her study uses monthly data in order to
analyze the effects of wind at O’Hare airport. Obviously this data will be less specific than
intended for finding a correlation between airline and automotive traffic. Both Li Shan-Mei’s
and Lewis’ articles will be used to see what can be expected in fluctuations in airline traffic.
Lewis starts out by going through the history of the airport and the uses of the runway. Her
goal is to show the effective use of the runway in relation to the wind. Each runway is mapped
using GIS to show how it is used (airline arrivals and departures). Airline traffic is measured
each month for seven different years (Lewis, pg 39-41). The data used is not very specific but
covers a large time period. Lewis’s study can be helpful in determining when to record data of
8
airline traffic. Some months may be different from others in terms of airline traffic. It is
important to record data that is not influenced by outside factors (wind / weather, time of day).
Lewis and Li Shan-Mei’s studies provide valuable information to keep in mind in order to
find a certain time period to record data. Mei found that weather can affect airlines differently
depending on the month. For example the weather in June affects flights more than in April (Li
Shan Mei, Pg 5). The affects of weather and other outside influences need to be considered
when trying to find a correlation between airline and road traffic. Both Lewis and Li Shan-Mei’s
articles can be expanded upon by using their data and comparing it to hourly road traffic since
they only analyze airline traffic.
Automotive traffic near O’Hare, just like airline traffic, is influenced by many events.
This is important to note in this thesis since unusual events that influence data should be
avoided. It is important to look at a time period that is similar to the average pattern
throughout any year at O’Hare. For example around Christmas traffic patterns around O’Hare
may change drastically over average yearly conditions. Airline traffic, severe weather, holidays
and other outside influences should be avoided when collecting data in order to eliminate as
many outside influences as possible. The less outside influences there are the easier it will be
to see what the correlation is between road and air traffic.
Overall the main contributions that the literature has made are techniques and ways to
use data in transportation geography. The articles explain the advantages and disadvantages of
using certain methods along with explaining some possible new methods. Obviously relating
road and air traffic is a field that can be expanded on since none of the articles found do so.
9
The articles found either relate to road or air traffic, but never both. Since the intention of this
thesis is to look at road and air traffic this might help uncovered new territory in GIS-T. One of
the main challenges to transportation and geography is finding data for GIS. Currently the
amount of data is limited, but it is growing. Finding data in the future is bound to become
easier since studies on the subject will increase over time according to current trends. These all
can be used in finding a correlation between road traffic levels near O’Hare and airline arrival
and departure times.
Problem Statement
The purpose of this project is to find whether or not there is a correlation between
hourly highway traffic volume near O’Hare and the number of airline arrivals and departures
over a 24-hour period.
To be more specific, four sets of alternative (H1) and null (H0) hypotheses will be tested.

#1
o H1: Road traffic levels will increase after airline arrivals
o H0: Road traffic levels will not increase

#2
o H1: Road traffic levels will increase before airline departures
o H0: Road traffic levels will not increase

#3
o H1: The further away the longer the time lag is between road and air traffic
o H0: Time lag will not increase with distance away from the airport
10

#4
o H1: The closer to the airport the stronger the correlation will be between road
and air traffic
o H0: The correlation between road and air traffic will not decrease with distance
from the airport
Methodology
Data Acquisition
This thesis will be looking at road and air traffic count at Chicago O’Hare airport. The
arrival and departure times for each flight at Chicago O’Hare will be used. Traffic will be
recorded for a number of specific points located on highways around O’Hare airport. Once all
the data is collected, the air and road traffic count will be recorded in a time series and mapped
in ArcGIS. A correlation coefficient analysis and a significance testing will be run in order to tell
if there is a statistically significant correlation between the time series of air and road traffic
and if yes how strong it is.
The main data source that will be used for traffic count is acquired from the Illinois
Department of Transportation (IDOT). Traffic levels will be recorded for 24 hours each day and
will be recorded on six different days. This data will then be stored in Excel™ so it can be
compared to air traffic. A total of six traffic points will be used that are located on highways
around Chicago O’Hare. Four of the points will be located at a distance under an hour drive
from the airport. The other two will be located at a distance about an hour or more drive from
the airport.
11
The points that are under an hour drive (short distance) to the airport should see the
most correlation with air traffic than the points further than an hour away (long distance). The
reason the traffic points are separated by under an hour and over an hour from the airport is
because the traffic data is recorded by hour. If a long distance point is under an hour away
from the airport its data might overlap with that of the short distance point. There will be
many more influences other than O’Hare for the long distance points since they are located
further from the airport. However, the long distance points serve a very important purpose of
acting as a control for this research. By comparing the correlations of the long distance points
and the short distance points it will be easier to see how the airport influences the traffic.
Airline traffic from O’Hare will be obtained from Research and Innovative Technologies
Administration (RITA). The scheduled times for each arrival and departure flight will be used.
Using actual flight times would not make sense since people travel to the airport expecting to
leave at the scheduled time. People determine what time they leave based on the scheduled
time of their flight. Each airline arrival and departure count at O’Hare will be recorded 24
hours a day. There are many airlines at O’Hare, but only the major (most flights per day)
airlines will be used. A total of eleven airline companies will be used in this thesis. These
airlines include: American Airlines, American Eagle, Atlantic South East Airlines, Continental
Airlines, Delta Airlines, ExpressJet Airlines, JetBlue Airways, Mesa Airlines, SkyWest Airlines,
United Airlines and US Airlines. The number of arrivals and departures of each airline will need
to be recorded and transferred to an Excel™ table.
12
Correlation Analysis
In order to determine if there is a correlation between air and road traffic, the
correlation coefficient (r) between airline arrivals and departures and road traffic was
calculated. Separate correlation tests were conducted for the points located in the “short
distance” category and the “long distance” category. The correlation coefficient for each data
set is tested using a two tailed t-test. The t-test gives a certain t value. There are critical values
that the t value must attain in order for there to be enough statistical evidence to show a
significant correlation. Given the alpha value and the degrees of freedom the critical value of
the data set can be determined. If the t value falls above the critical value there is a 99%
probability it did not occur by chance. An r value can be between 1 and -1. The closer it is to 1
the higher degree of a positive correlation. The closer it is to -1 the more negative the
correlation.
A few different correlation coefficients will be calculated for each point in order to find
the strongest r value. Multiple shifts in time for traffic will be used in order to compare the r
values. When comparing traffic with airline arrivals, traffic times will be shifted ahead a certain
number of hours. For example a one hour shift ahead would be traffic levels at 15:00 – 16:00
compared to airline arrivals at 14:00 – 15:00. When comparing traffic with airline departures,
traffic times will be shifted back a certain number of hours. An example of a one hour shift
back would be traffic levels at 13:00 – 14:00 compared to airline departures at 14:00 – 15:00. It
is important to test these shifts in time since airline arrivals and departures at a certain time
might affect traffic level at a different time. For example, passengers for flights that are
departing will need to arrive at the airport well ahead of their flight’s departure. By testing
13
different shifts in time it will be possible to see a correlation of how early passengers have to
arrive. Traffic level could be affected by the airline departure an hour, two hours or some other
time before it takes off. By using these different time shifts when testing r, it is possible to
determine whether or not to reject null hypothesis one and null hypothesis two. By comparing
the r values of the long and short points, it will be possible to determine whether or not to
reject null hypothesis three and null hypothesis four.
Traffic Count Sample Points
Each point that was chosen for a traffic count is shown in Figure 1 below and the
Appendix in more detail. There were a total of six traffic count points on different days located
a certain distance from the airport. It is important to note that the traffic data from some
sample points was recorded on different days than other points. All the traffic count data was
recorded within the same year, but some points have data from a different month. The traffic
data was limited to only certain days; therefore, all the recorded traffic data are not within the
same month. If traffic data for several days occurring in the same week / month were recorded
this could have increased accuracy. Obtaining traffic data that is closer together in time likely
increases accuracy because there are probable seasonal and weekly fluctuations of traffic levels
throughout the year. The fluctuations can occur from weather, time of the season, events,
week day vs. weekend, holidays, etc. By having data that is recorded on the same day of the
week or month, it reduces the chances of having more outside influences (other than airline
arrivals and departures) affecting the traffic level. Unfortunately, due to how limited the data
was, some points were recorded in different months.
14
Four of the points were located less than 15 miles from the airport. Two other traffic
count points were chosen that were further than 39 miles from the airport. The distance from
the airport refers to the driving distance the cars would have to travel on the highways. Traffic
count points located closer to the airport should have less additional influences beyond airline
departures and arrivals than traffic points located further away. The data from these points
were organized into a “short distance” vs. a “long distance” group in testing the correlation
coefficient (r) between the highway traffic and airline departures and arrivals.
15
Figure 1 - Location of all 6 sample points
Figure 1 on the previous page shows all six traffic sample points. The red airplane
shown on the map is the location of Chicago O’Hare airport. Each traffic point is marked with a
16
yellow dot and labeled with the distance it is from the airport. For a more detailed map of
where each traffic point is located, refer to the appendix.
The point that is located .6 miles from the entrance of O’Hare Airport most likely
contains only cars traveling to and from the airport. It is located on a section of highway that is
in the O’Hare area. There are no other paths that cars at this point travel to other than from or
to the airport. This can be considered the most reliable point since it has no other outside
influences. Traffic for this point was recorded on 6/14/2011. The point located 3.2 miles from
the airport entrance is on Kennedy Expressway. This is the second closest traffic count location.
The traffic data for this point was recorded on 6/09/2011. Traffic for the point located 9.4 miles
away on Kennedy Expressway was recorded on 8/01/2011. The final point in the “close to the
airport” category is located 12.6 miles from the airport on Dwight D. Eisenhower Expressway.
This traffic data was recorded on 6/27/2011. The 40 mile point on the map is in the “long
distance” category and is located on I-94/US-41 north of the airport. Its data was recorded on
4/18/2011. This point is probably around an hour drive from the airport. If it were possible, a
point further than this would have been chosen, but this was the furthest point that could be
found located on I-94/US-41. This point might be close to overlapping with the short distance
category, but due to the limitations in data this point needs to be used for a long distance point.
The second “long distance” point is located 79 miles south of the airport (as shown on the map)
on I-57. This point is located well over an hour drive from the airport. The data for this point is
unlikely to overlap with the close distance points data. The data for this point was recorded on
6/22/2011.
17
The traffic data recorded at each point is split into to and from the airport. For example
points that are located east of the airport are split into westbound traffic and eastbound traffic.
Westbound traffic would be traveling toward the airport and eastbound traffic would be
traveling away from the airport. Traffic traveling toward the airport will be compared to
departure flights since passengers will be traveling toward the airport for their flights. Traffic
that is traveling away from the airport will be compared to airline arrivals since passengers will
be leaving the airport from their flights.
18
Results
Airline Departures and Traffic Count Comparison
Below are multiple graphs (Figures 2-7) comparing the airline departures each day with
the traffic count at each highway sample point. The graphs below display the hourly traffic
count and airline departures displayed on one graph on the same day. The graphs show hourly
data of each individual day from 01:00 (1 am) to 24:00 (midnight). The traffic data in the graphs
are not shifted. In other words, traffic that occurred at 07:00-0:800 is compared to airline
arrivals that happened at 07:00 - 08:00. By displaying the traffic count and airline departures
in one graph it is easier to see a possible correlation between the two and the corresponding
time lag between the two.
4000
80
3500
70
3000
60
2500
50
2000
40
1500
30
1000
20
500
10
0
0
1
3
5
7
9
Airline Departures Per Hour
Traffic Count Per Hour
Airline Departures and 0.6 Mile Point (6/14)
Traffic Count (.6 Mile Point)
Airlines Departures
11 13 15 17 19 21 23
Time of Day (Hours)
Figure 2 - Airline Departures compared to the traffic count on 6/14/2011 for the point located 0.6 miles away on the
Kennedy Expressway
19
7000
80
6000
70
5000
60
50
4000
40
3000
30
2000
20
1000
10
0
0
1
3
5
7
Airline Departures Per Hour
Traffic Count Per Hour
Airline Departures and 3.2 Mile Point (6/09)
Traffic Count (3.2 Mile Point)
Airlines Departures
9 11 13 15 17 19 21 23
Time of Day (Hours)
Figure 3 – Airline departures compared to the traffic count on 6/09/2011 for the point located 3.2 miles away
Traffic Count Per Hour
10000
80
70
8000
60
50
6000
40
4000
30
20
2000
10
0
0
1
3
5
7
Airline Departures Per Hour
Airline Departures and 9.4 Mile Point (8/01)
Traffic Count (9.4 Mile Point)
Airlines Departures
9 11 13 15 17 19 21 23
Time of Day (Hours)
Figure 4 - Airline departures compared to the traffic count on 8/01/2011 for the point located 9.4 miles away
20
7000
80
6000
70
5000
60
50
4000
40
3000
30
2000
20
1000
10
0
0
1
3
5
7
Airline Departures Per Hour
Traffic Count Per Hour
Airline Departures and 12.6 Mile Point (6/27)
Traffic Count (12.6 Mile Point)
Airlines Departures
9 11 13 15 17 19 21 23
Time of Day (Hours)
Figure 5 - Airline departures compared to the traffic count on 6/27/2011 for the point located 12.6 miles away
Traffic Count Per Hour
3500
90
80
70
60
50
40
30
20
10
0
3000
2500
2000
1500
1000
500
0
1
3
5
7
Airline Departures Per Hour
Airline Departures and 40 Mile Point (4/18)
Traffic Count (40 Mile Point)
Airlines Departures
9 11 13 15 17 19 21 23
Time of Day (Hours)
Figure 6 - Airline departures compared to the traffic count on 4/18/2011 for the point located 40 miles away
21
800
80
700
70
600
60
500
50
400
40
300
30
200
20
100
10
0
0
1
3
5
7
9
Airline Departures Per Hour
Traffic Count Per Hour
Airline Departures and 79 Mile Point (6/22)
Traffic Count (79 Mile Point)
Airlines Departures
11 13 15 17 19 21 23
Time of Day (Hours)
Figure 7 - Airline departures compared to the traffic count on 6/22/2011 for the point located 79 miles away
22
Airline Arrivals and Traffic Count Comparison
Below are multiple graphs (Figures 8-13) comparing the airline arrivals each day with the
traffic count at each sample point. Just like the airline departure graphs the hourly traffic data
for the graphs below are not shifted. By displaying the traffic data and airline arrivals right next
to each other it provides a better perspective on the correlation and possible time lag.
3500
80
3000
70
2500
60
50
2000
40
1500
30
1000
20
500
10
0
0
1
3
5
7
Airline Arrivals Per Hour
Traffic Count Per Hour
Airline Arrivals and 0.6 Mile Point (6/14)
Traffic Count (.6 Mile Point)
Airlines Arrivals
9 11 13 15 17 19 21 23
Time of Day (Hours)
Figure 8 - Airline Arrivals compared to the traffic count on 6/14/2011 for the point located .6 miles away
23
7000
80
6000
70
5000
60
50
4000
40
3000
30
2000
20
1000
10
0
Airline Arrivals Per Hour
Traffic Count Per Hour
Airline Arrivals and 3.2 Mile Point (6/09)
Traffic Count (3.2 Mile Point)
Airlines Arrivals
0
1
3
5
7
9 11 13 15 17 19 21 23
Time of Day (Hours)
Figure 9 - Airline Arrivals compared to the traffic count on 6/09/2011 for the point located 3.2 miles away
8000
80
7000
70
6000
60
5000
50
4000
40
3000
30
2000
20
1000
10
0
Airline Arrivals Per Hour
Traffic Count Per Hour
Airline Arrivals and 9.4 Mile Point (8/01)
Traffic Count (9.4 Mile Point)
Airlines Arrivals
0
1
3
5
7
9 11 13 15 17 19 21 23
Time of Day (Hours)
Figure 10 - Airline Arrivals compared to the traffic count on 8/01/2011 for the point located 9.4 miles away
24
Airline Arrivals and 12.6 Mile Point (6/27)
80
70
5000
60
4000
50
3000
40
30
2000
20
1000
10
0
Airline Arrivals Per Hour
Traffic Count Per Hour
6000
Traffic Count (12.6 Mile Point)
Airlines Arrivals
0
1
3
5
7
9 11 13 15 17 19 21 23
Time of Day (Hours)
Figure 11 - Airline Arrivals compared to the traffic count on 6/27/2011 for the point located 12.6 miles away
4000
80
3500
70
3000
60
2500
50
2000
40
1500
30
1000
20
500
10
0
Airline Arrivals Per Hour
Traffic Count Per Hour
Airlines Arrivals and 40 Mile Point (4/18)
Traffic Count (40 Mile Point)
Airlines Arrivals
0
1
3
5
7
9 11 13 15 17 19 21 23
Time of Day (Hours)
Figure 12 - Airline Arrivals compared to the traffic count on 4/18/2011 for the point located 40 miles away
25
800
80
700
70
600
60
500
50
400
40
300
30
200
20
100
10
0
Airline Arrivals Per Hour
Traffic Count Per Hour
Airline Arrivals and 79 Mile Point (6/22)
Traffic Count (79 Mile Point)
Airlines Arrivals
0
1
3
5
7
9
11 13 15 17 19 21 23
Time of Day (Hours)
Figure 13 - Airline Arrivals compared to the traffic count on 6/22/2011 for the point located 79 miles away
26
Correlation Analysis
Airline Departures and Short Distance Traffic
The strongest r value for short distance traffic points compared to airline departures
were when the time for traffic was shifted back one hour and compared to airline departures.
The r value highlighted in red in table 1 below has the strongest correlation. An r value of .751
was found. The r value was then used in a t-test resulting in a t value of 11.031. The critical
value for the t-test is 2.640 (as shown in table 1). Since the t-value is greater than the critical
value there is ample evidence of a correlation between the data for airline departures and
traffic near (short distance points) the airport. As seen in the table below there is still a
correlation for the other values. However, the strongest and most statistically significant
correlation is when the time for the traffic was shifted back one hour.
Table 1
Correlation
0.298111838
0.519575585
0.688266089
0.751108287
0.702653923
Time Shift
t Value Critical Value
Shifted 4 hours back 3.028
2.64
Shifted 3 hours back 5.896
2.64
Shifted 2 hours back 9.198
2.64
Shifted 1 hour back 11.031
2.64
not shifted
9.574
2.64
27
Airline Departures and Long Distance Traffic
Table 2 below shows the r values that were calculated for long distance traffic points
with airline departures. The strongest r value was about .525 when the time was shifted one
hour back for traffic data. After running a t-test, a t value of 4.186 was found for this strongest
r value. Since this falls above the critical value of 2.680 this indicates there is a statistical
significant correlation between long distance traffic and airline departures. The t value of 4.186
is not as strong as the t value of 11.031 that was found for the short distance points. This
means that the short distance points have stronger and statistical more significant correlation
with the number of airline departures. Also there was a correlation when the time was not
shifted, shifted 2 and 3 hours back. These correlations were not as strong and significant as
when the time was shifted 1 hour back.
Table 2
Correlation
-0.08452106
0.084719838
0.240464203
0.378802084
0.495252036
0.525245289
0.448276451
Time Shift
Shifted 6 hours back
Shifted 5 hours back
Shifted 4 hours back
Shifted 3 hours back
Shifted 2 hours back
Shifted 1 hour back
not shifted
t Value Critical Value
-0.575
2.68
0.577
2.68
1.68
2.68
2.776
2.68
3.866
2.68
4.186
2.68
3.401
2.68
28
Airline Arrivals and Short Distance Traffic
Table 3 below shows the r values that were calculated for short distance traffic points
with airline arrivals. The strongest r value was about .650 (highlighted in red) when the time
was not shifted for traffic data. The critical value is 2.64 for this data set. A t value of 8.287 was
calculated. It can be concluded that there is a statistical significant correlation between short
distance traffic points and airline arrivals because the t value is greater than the critical value. A
correlation was found for when the time was shifted 1, 2, and 3 hours ahead. The correlations
for these were not as strong as the correlation when the time was not shifted.
Table 3
Correlation Time Shift
t Value Critical Value
0.140618457 Shifted 4 hours ahead
1.377
2.64
0.300181239 Shifted 3 hours ahead
3.051
2.64
0.45151347 Shifted 2 hours ahead
4.906
2.64
0.564560077 Shifted 1 hour ahead
6.632
2.64
0.649737596 not shifted
8.287
2.64
29
Airline Arrivals and Long Distance Traffic
Table 4 below shows the r values for long distance traffic points and airline arrivals. The
greatest r value is about .513 (highlighted in red). A t value of 4.058 was calculated. Since the t
value is above the critical level of 2.68 there is a correlation between the data. A t value of
4.058 is not as strong as the t value of 8.287 from the short distance traffic points. This means
that the correlation between long distance traffic points and airline arrivals is weaker and less
statistical significant than short distance traffic and airline arrivals. When the time was shifted
1 hour for the long distance points they showed an even weaker and less statistical significant
correlation.
Table 4
Correlation
-0.032177503
0.083592201
0.173265718
0.224506934
0.33081925
0.443231066
0.51341309
Time Shift
Shifted 6 hours ahead
Shifted 5 hours ahead
Shifted 4 hours ahead
Shifted 3 hours ahead
Shifted 2 hours ahead
Shifted 1 hour ahead
not shifted
t Value
-0.218
0.569
1.193
1.563
2.378
3.354
4.058
Critical Level
2.68
2.68
2.68
2.68
2.68
2.68
2.68
30
Conclusion
Discussion
The results suggest a correlation between road and airline traffic. The correlation
depends on the distance the traffic point is from the airport and whether airlines are arriving or
departing.
For airline arrivals and short distance traffic, the strongest correlation with a t value of
8.287 was found when there was no time shift. Although there was still a correlation when
times for road traffic were shifted one, two and three hours ahead, the strongest correlation
was when there was no shift. Since no shift in time had the strongest correlation it is used in
testing hypotheses number 1. For hypothesis #1, H1 was that road traffic level would increase
after airline arrivals while H0 was that road traffic level would not increase. Since the most
significant and strongest correlation between traffic and airline arrivals was positive, H0 is
rejected and the alternative hypothesis (H1) is accepted. According to the results, the strongest
correlation is found within the same hour (no time shift/lag). A likely explanation is that
passengers are leaving the airport and passing by the short distance traffic count points within
an hour. Since traffic levels are measured hourly it is possible that the passengers could be
getting to the short distance points before the next hour of traffic is collected.
Airline arrivals and long distance traffic showed the strongest correlation with a t value
of 4.058 when there was no time shift. When the time was shifted one hour ahead there was a
correlation, but not as significant and strong as no shift in time. For hypothesis #1, H0 is
31
rejected and H1 is accepted since the strongest correlation is positive. However, the strongest
correlation is still found within the same hour (no time shift/lag). There might be a variety of
reasons why this occurred. These points were located far from the airport and it would be
highly unlikely that passengers passed by these points less than an hour after their plane
arrived since these points are located about an hour or more from the airport. It is possible
that the 40 mile point could be less than an hour away, but this would require passengers to
leave from the airport almost immediately following their airline arrival. This is highly unlikely
since most passengers have to wait for baggage and walk a certain distance through the airport.
However, the results show the strongest correlation of traffic and airline arrivals at the long
distance points is within the same hour that the planes arrive. This could be due to other
outside influences since these points are located 40 and 79 miles from the airport. There are
many other factors that can affect the road traffic count of these long distance points from the
airport. The more space there is between a traffic count point and the airport the greater the
possibilities that there will be other influences. The strongest correlation at no time shift for
these points must have occurred from other outside influences between the traffic points and
the airport. For hypothesis #3, H1 was that the further away the traffic point, the longer the
time lag is between road and air traffic, while H0 was that the time lag would not increase with
distance away from the airport. From the results H1 is rejected and H0 is accepted. There is not
enough evidence to show that the further away the longer the time lag is between road traffic
and the number of airline arrivals.
Airline departures and short distance traffic showed the strongest correlation with a t
value of 11.031 when the time was shifted back one hour. In addition, there was a correlation
32
when the time was not shifted back and shifted two, three, and four hours back. Since one
hour showed the strongest correlation it was chosen to test the hypotheses. For hypothesis #2,
H1 was that road traffic levels would increase before airline departures and H0 was that road
traffic levels would not increase. According to the results, the traffic level showed the strongest
and positive correlation one hour before the airlines departed. So H1 was accepted and H0 was
rejected. The data shows a clear traffic increase an hour before airlines depart. This is most
likely due to passengers trying to catch their flight on time. Obviously if a passenger arrives
after a plane departs or at the time it departs they will miss their flight so these results make
sense in the passenger perspective. The potential reason for a correlation with other shifts is
passengers arriving earlier than an hour before their flight. The correlation decreases with time
most likely because fewer passengers arrive more than an hour earlier. A correlation was also
found for when the time was not shifted. This could be from passengers arriving within an hour
of their flight departing, but the correlation is not as strong.
Airline departures and long distance traffic showed the strongest correlation with a t
value of 4.186 when the time for traffic was shifted back one hour. Correlations could still be
seen for no shift in time, two, and three hours back in time. Since the strongest correlation is
when the traffic time was shifted back one hour, hypothesis #2 H1 was accepted and H0 was
rejected. There is possible explanation for this short time lag between the traffic levels at long
distance points and airline departures. Since the time was shifted one hour back this compares
traffic that occurs between one and two hours before the airlines depart. This would most
likely give passengers enough time to travel from the long distance traffic points to the airport
in time to catch their flight. It is important to note that the strongest correlation for airline
33
departures and long distance traffic has the same time shift (one hour back) as the airline
departures and short distance traffic. The traffic levels for the long distance points might have
a correlation with the airline departures closer to two hours before the airlines depart.
However, if the correlation is slightly under two hours it would fall in the one hour shifted back
category. This is a possible explanation why the strongest correlation for long and short points
occurred when the time was shifted 1 hour back. From these results hypothesis #3 H 1 is
rejected and H0 is accepted. The time lag did not appear to increase with distance from the
airport, at least not at the temporal resolution of one hour. Without additional data at the
distant traffic points between one and two hours before airline departures, there is not enough
evidence to support H1.
For the last hypothesis (#4), H1 is that the closer the traffic points are to the airport the
stronger the correlation will be between road and air traffic; while H0 is that the correlation
between road and air traffic will not decrease with distance from the airport. When airline
arrival and departure times were compared with traffic levels, their respective correlations
decreased with increasing distance. Road traffic levels also had more significant correlations
with air traffic when using close traffic points over long distance traffic points. This supports H1
and therefore H1 is accepted and H0 is rejected. The closer the traffic points were to the airport
the stronger correlation there was between road and air traffic. The probable reason for the
correlation increase was because there was less outside influences other than airline traffic at
the short distance traffic points than the long distance traffic points.
34
Outlook and Future Directions
This study and the data it provides could be very helpful in helping highway and airport
designers and planners. Highway designers might have a better understanding of how traffic is
affected around airports and therefore they will have an easier time deciding the type and size
of highways of the future. The study also provides a general idea of when people start to arrive
for their flights and when they leave. By knowing when people are leaving and arriving at the
airports it can be possible to ascertain how to avoid high traffic that is due to airline departures
and arrivals. Knowing how to avoid high traffic periods could be very useful for many
passengers and could possibly improve traffic flow since some might try to travel when it is less
congested according to this study. Further, the methods used in this study could be used to
predict traffic in near real time, and streamed on the Internet for trip planning. This service
could potentially improve traffic surges as people attempt to avoid predictable peaks.
There are a few different ways that this study can be improved in the future. The traffic
data source that was used (Illinois Department of Transportation) was limited in the number of
days and locations road traffic was recorded. If the number of days and locations of traffic
counts could be increased, this would increase the accuracy of the study. Due to the limitations
of the data it was impossible to obtain traffic data that was on the same day, or the same day of
the week. The closer in time the traffic data is recorded the more accurate the study would be.
Another way this study could be improved is if traffic data was recorded more often than every
hour. The accuracy would be improved if traffic data could be broken down into half hour
35
blocks, 20 minute blocks, etc. Once more data becomes available in the future for GIS and
transportation these improvements might become possible.
36
Acknowledgements
I would like to thank my thesis advisor Dr. Wenjie Sun for her enormous help in guiding
me through my thesis. Without the help of Dr. Sun, completing my thesis would have been
very difficult. Secondly I would like to thank Dr. Kurt Piepenburg for his assistance in guiding
me through my thesis. I would also like to thank my family for their constant support and
assistance. Also I would like to thank the entire Geography and Earth Science department for
their guidance and providing academic knowledge in the courses I have taken here at Carthage
College.
Appendix
37
38
39
40
41
References

“Processing traffic data for statistical use; The Norwegian methods” by Roar Norvik.
Norwegian Public Roads Administration. Department of Technology. Centre for Road
and Traffic Technology, Trondheim.

Using a GIS-Based Approach and Wind Rose to Determine Runway Effectiveness and
Study the Impacts of O’Hare Chicago International Airport by Patrici A L. Danyelle Lewis

“The Geography of Transportation Systems” by Jean-Paul Rodrigue, Claude Comtois and
Brian Slack (2009), New York: Routledge.
http://people.hofstra.edu/geotrans/eng/content.html

“Fluctuations in airport arrival and departure traffic: A network analysis” by Li Shan-Mei,
Xu Xiao-Hao, and Meng Ling-Hang (2012). School of Computer Science and Technology,
Tianjin University, Tianjin 300072, China. College of Air Traffic Management, Civil
Aviation University of China, Tianjin 300300, China.
 Research and Innovative Technologies Administration
http://www.bts.gov/programs/airline_information/
 Illinois Department of Transportation Traffic Count Database System (TCDS)
 http://www.ms2soft.com/tcds/tsearch.asp?loc=Idot&mo
 “The ESRI Guide to GIS Analysis Volume 2: Spatial Measures & Statistics” by Andy Mitchell
(2005), ESRI.
42