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Development of a driving cycle for intra-city
buses in Chennai, India
Article in Atmospheric Environment · October 2011
DOI: 10.1016/j.atmosenv.2011.06.067
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Development of a driving cycle for intra-city buses in Chennai, India
K.S. Nesamani a, *, K.P. Subramanian b,1
a
b
Institute of Transportation Studies, 4000 Anteater Instruction and Research Bldg (AIRB), University of California, Irvine, CA 92697, USA
Urban Systems Engineering, Anna University, Chennai 600 035, India
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 20 March 2011
Received in revised form
8 June 2011
Accepted 23 June 2011
In India the emissions rate and fuel consumption of intra-city buses are estimated using the European
driving cycles, which don’t represent Indian driving conditions and in-use operation of vehicles. This
leads to underestimation or overestimation of emissions and fuel consumption. In this context, this
paper offers some insight into the driving characteristics of intra-city buses using a Global Positioning
System. The study has revealed that irrespective of road type and time of travel, a higher percentage of
time is spent in idle mode. This is primarily due to alighting and boarding of passengers at regular
intervals and fixed delays caused by traffic lights. More than 90 percent of trips have an average speed of
less than 30 km h1. This study has also developed an intra-city bus driving cycle for Chennai and
compared it with some well-known international driving cycles. It has revealed that Chennai has unique
driving characteristics and, therefore, it may not be appropriate to adopt a driving cycle of another
country or city.
Ó 2011 Elsevier Ltd. All rights reserved.
Keywords:
Driving cycle
Driving characteristics
Intra-city buses
Global positioning system
Chennai
1. Introduction
Buses are environmentally friendly in terms of per capita energy
consumption and emission. They are relatively inexpensive and
provide a low level of service and comfort. States governmentowned buses provide public road transportation services in India.
The modal share of buses ranges from 5 to 40 percent in urban
cities (WSA, 2008). The average age of buses in India ranges
between 3.25 years and 10.33 years (Ramasaamy, 2006). Average
trip lengths in cities have increased significantly due to urban
sprawl. Consequently, this has increased the number of motor
vehicles about 183 times over the past five decades whereas the
number of buses increased only by 17 times during the same period
(Nesamani, 2010). Due to the buses’ old age, obsolete technology,
poor maintenance, bad fuel quality, and overloading have increased
emissions from buses in India.
In India, buses are manufactured using truck engines and chassis
that are not suitable for urban driving conditions. They predominantly consume diesel fuel due to the inherent advantage of efficiency, reliability, and durability. Diesel-fueled vehicles emit
a greater amount of oxides of nitrogen (NOx) and particulate matter
(PM) due to high combustion temperature. NOx contributes to
* Corresponding author. Tel.: þ1 949 701 8217; fax: þ1 949 824 8385.
E-mail addresses: ksnesa55@hotmail.com (K.S. Nesamani), kps_subbu1@yahoo.
co.in (K.P. Subramanian).
1
Tel.: þ91 44 22581603.
1352-2310/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.atmosenv.2011.06.067
ozone formation, and diesel PM especially particles of less than 10
microns in diameter are carcinogenic in nature. A study by
Kirchstetter et al. (1999) found that heavy-duty diesel vehicles emit
15e20 times more particles when compared to light-duty vehicles.
There are several studies that have characterized the health and
environmental impacts of diesel exhaust and other particulates
(Mohanraj and Azeez, 2004; Hoek et al., 2010).
1.1. Need for exclusive driving cycles
Driving cycle is a sequence of operating conditions (idle, acceleration, deceleration, and cruise) developed to represent a typical
driving pattern of a city. In India, driving cycles are adopted from
Europe. Hence, for the purpose of type approval, certification of
vehicles, and to estimate exhaust emissions European testing
procedures are employed. Heavy-duty engines are tested on an
engine dynamometer in a steady state condition using the
Economic Commission for Europe Regulation No. 49 (ECE R49)
procedure for the purpose of certifications. The test is comprised of
13 sequence modes defined by different settings of speed and load.
The main reason for not testing the complete vehicle is that certain
engines are used for different applications. As the name indicates,
emissions are measured at fixed speed/load operating conditions.
The major limitation of the steady state test is that it does not
capture the transient conditions changing from one operation to
another. Hence, from 2010 onwards, Euro III buses are required to
certify under the European Transient Cycle (ETC) in addition to the
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K.S. Nesamani, K.P. Subramanian / Atmospheric Environment 45 (2011) 5469e5476
European Stationary Cycle (ESC). The ESC is similar to the ECE R49 test
except that load and weight factors have changed. ETC is based on the
real-world measurement in Europe that captures three driving
patterns, including urban, rural, and motorway. The total cycle period
is 1800 s with each driving pattern 600 s (Arregle et al., 2006). These
tests are supposed to represent real-world driving conditions and
create repeatable measurements, but in reality, none of these driving
cycles represents Indian traffic conditions. Therefore, developing
exclusive driving cycles is an indispensible requirement to enable
better estimation of emissions and fuel consumption.
Vehicle design, traffic characteristics, geometric design, and
roadside environment in general strongly affect emissions per
kilometer (Ericsson, 2000; Nesamani et al., 2007). Hence, ARAI
(Automotive Research Association of India) in India has developed
a facility to measure the fuel consumption and mass emissions of
in-use heavy-duty vehicles using the chassis dynamometer. ARAI
has developed two heavy-duty driving cycles, Delhi Bus Driving
Cycle (DBCD) and an Overall Bus Driving Cycle (OBDC) based on
data collected in Delhi as shown in Fig. 1. Both driving cycles have
similar characteristics with time periods of 170 and 150 s respectively (Badusha and Ghosh, 1999; ARAI, 2007). However, these
cycles are developed with constant speed and acceleration profiles,
which do not reflect real-world driving patterns. Pelkmans et al.
(2001) found that real-city traffic and simulated city cycles differ
from technology to technology. Therefore, researchers have focused
on measuring the real-world emission using the on-board
measurement system. From Euro VI regulation onwards, it is
required to verify real-world emissions using the portable emissions measurement system. Table 1 shows the timeline to implement the emission standards for heavy-duty vehicles in India and
the European Union. It shows that India is lagging behind about
eight to 10 years in implementing these standards.
Several efforts have been made recently in India to analyze
driving characteristics of light-duty vehicles (two-wheelers, cars)
in various cities (Nesamani and Subramanian, 2006; Kamble et al.,
2009). However, the knowledge of bus driving characteristics in
India is very limited. Therefore, the main objective of this study is to
examine operating characteristics of intra-city buses in Chennai,
India using Global Positioning System (GPS). The second objective
is to develop a driving cycle based on data collected second-bysecond and to compare mean values of newly developed driving
characteristics with those for conventional and common driving
cycles. Long-distance buses are not considered as part of this study
since they have different operating conditions.
Table 1
Implementation of emission standards for heavy-duty vehicles in India and the
European Union.
Major cities
Nationwide
Euro
Euro
Euro
Euro
Euro
Euro
2000
2001
2005
2010
Not yet decided
Not yet decided
2000
2003
2005
2010
Not yet decided
Not yet decided
2000
2005
2010
Not yet decided
Not yet decided
Not yet decided
b 60
Delhi Bus Driving Cycle (DBDC)
50
Speed (kph)
Speed (kph)
NCR (National
Capital Region)
I
II
III
IV
V
VI
40
30
20
Overall Bus Driving Cycle
50
40
30
20
10
10
0
0
50
100
Time (sec)
150
European
Union
1992
1996
2000
2005
2008
2013
Average speed (V1) e Average speed of entire trips (km h1)
Average running speed (V2) e Average speed of entire trips
excluding idle time (km h1)
Maximum speed (Vmax) e Maximum speed of entire trips
(km h1)
Average acceleration (Acc) e Average acceleration of entire
trips (m s2)
Average deceleration (Dec) e Average deceleration of entire
trips (m s2)
Maximum acceleration (Accmax) e Maximum acceleration of
entire trips (m s2)
Maximum Deceleration (Decmax) e Maximum deceleration of
entire trips (m s2)
Percentage of time spent in idle mode (Pi) e Speed equals zero
(%)
Percentage of time spent in acceleration mode (Pa) e Speed
greater than 5 km h1 and acceleration greater than 0.1 m s2
(%)
Percentage of time spent in deceleration mode (Pd) e Same as
acceleration except that acceleration should be negative (%)
Percentage of time spent in creeping mode (Pcr) e Speed less
than 5 km h1 and acceleration and deceleration should be less
than 0.1 m s2 (%)
Percentage of time spent in cruise mode (Pc) e Speed greater
than 5 km h1 and acceleration and deceleration should be
greater than 0.1 m s2 (%)
Fig. 2 illustrates the methodology adopted in this study to
analyze the driving characteristics of intra-city buses. Different
parameters used to describe the driving characteristics were
identified from the literature review as a first step. The second step
0
India
was to estimate and analyze the mean value of the driving characteristics of test runs. The third step was to develop a driving cycle
using the micro-trips derived from test runs. The fourth step was to
develop the speed-acceleration frequency distribution (SAFD) as
suggested by Watson et al. to analyze the distribution of speed and
acceleration of developed cycle (Watson, 1995). The last step was to
compare the estimated driving characteristics with the international driving cycles of urban buses and analyze the differences.
Chennai city was selected as the case study area and GPS was used
to collect the speed and acceleration profile of buses.
The various parameters identified from the literature review to
describe the driving characteristics are as follows,
2. Methodology
a 60
Emission
standards
200
0
50
Fig. 1. In-use heavy-duty driving cycles in India.
100
Time (sec)
150
200
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5471
Route Selection based
on IRC & MTC route
Driving data collection
using GPS
Data quality check
Data analysis
Identify driving
parameters from literature
Estimate mean value of
driving characteristics
Analyze driving patterns
Develop SAFD
Identify international
driving cycles
Develop driving cycle
Compare driving cycles
Fig. 2. Study methodology.
Positive kinetic energy (PKE) e Energy required accelerating
P
2 Þ=Distance ðm s2 Þ
per unit distance. ðVi2 Vi1
Number of stops per km (Stop/km) e Number of stops per km
in a trip due to fixed and variable delays (No.)
Source: Kent et al., 1978; Kuhler and Karstens, 1978; Milkins and
Watson, 1983; Nesamani and Subramanian, 2006; Tong and Hung,
2010.
2.1. Travel characteristics of intra-city buses in Chennai
Intra-city buses in Chennai are owned and operated by the
Metropolitan Transport Corporation (MTC), a Government of Tamil
Nadu undertaking. It is fully owned by the State Government of
Tamil Nadu and has the monopoly to operate the intra-city bus
services. It is also rendering transport services to nooks and corners
of the Chennai Metropolitan Area and also provides feeder services
to suburban trains and MRTS. The MTC has a fleet of about 3421
buses. Average passenger occupancy throughout the trip length has
varied from 57 percent to 90 percent during the last four decades.
About 80 percent of bus commuters belong to the Low Income Group
(LIG) i.e., household with a monthly income of less than Rs. 5000,
according to a route rationalization study completed in 2001 (PTCS,
2001). The government of India has allocated funds under JNNURM
(Jawaharlal Nehru National Urban Renewal Mission) to procure new
buses to cater to the ever-increasing demand in the city.
Bus routes in Chennai have not been selected based on any
systematic criteria, rather they are based on impromptu conditions.
The Transport Corporation has a penchant to add new routes and to
expand the area of operation rather than augmenting the frequency
in existing routes and existing area of operation. Poor frequencies
entail loss of patronage, for passengers do not wait for long. The
average distance traversed by a bus is about 308 km per day. MTC
spends about Rs. 27.90 per km whereas it recovers only about 24.59
per km (CAG, 2010). The last fare hike was in the year 2001, when
the diesel price was Rs. 16 L1. The diesel price now is around Rs.
50 L1. Therefore, the MTC has been operating under heavy losses.
However, among all metropolitan transit agencies, MTC has the
highest cost recovery through fares (Badami and Haider, 2007). The
travel characteristics of MTC are as follows,
Average Age of buses e 2.2 years
No. of Routes e 690
No. of Passengers/day e 5.5 millions
Fuel efficiency e 4.5 km L1
Staff/bus e 7.0
Vehicle density/1 million population e 3.8
Fleet utilization e 92.1%
Occupancy Ratio e 80.2%
Accident/1 million km e 12.7
Source: mtcbus.org.
MTC buses account for mobile emissions of about 3 percent of
carbon monoxide (CO), 1 percent of volatile organic compounds
(VOC), 18 percent of oxides of nitrogen (NOx) and 6 percent of
particulate matter (PM) (Nesamani, 2010). The most common
problems of MTC buses in Chennai are overcrowding, low
frequency, crew indiscipline, and bunching. During peak hours,
buses have crush loading of 150 percent of the design capacity.
Fig. 3 depicts the awful mismatch between the demand and supply.
Urban sprawl has also taken its toll. Consequently, the modal share
of buses has declined from 54 percent in 1984 to 29 percent in
2008. The Second Master Plan for Chennai has set an ambitious
target to completely reverse the existing modal split between
public and personal modes of transportation. However, this will
remain on paper unless radical and out-of-the-box solutions are
attempted.
2.2. Data collection
Collection of real-world intra-city bus trajectories is critical for
this analysis. Broadly, there are two methods to collect the trajectories, including the chase-car technique and on-board measurement (Tong and Hung, 2010). The on-board measurement approach
was adopted in this study. Hence, with special permission from
MTC, buses in Chennai along the selected study corridors were
equipped with GPS to collect the trajectories. Study corridors were
selected by considering the land use, classification of roads, and
direction of traffic flow during peak periods. Six routes were
selected for this study to evaluate the driving patterns. The test
routes capture different land uses (schools, shops, residential),
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K.S. Nesamani, K.P. Subramanian / Atmospheric Environment 45 (2011) 5469e5476
Fig. 3. Overcrowding in MTC buses during peak hours (Source: thehindu.com).
traffic conditions (highly congested corridors, free flow), and
widths of roads (narrow and wider). The purpose of data collection
was not disclosed to drivers lest their driving behavior be influenced. Positional information and trajectory of the vehicle was
recorded second-by-second. A person on-board noted the route
number, odometer reading, and causes for individual stops (due to
traffic light/congestion/crossing traffic/parking or un-parking or
bus stop). Characteristics of test routes are shown below,
it has generated usable data elements of about 64,800 s. It was also
determined that there were no unusual conditions such as major
processions, VIP visits, or other activities that could induce
abnormal traffic characteristics in the selected corridor during the
survey. Data were downloaded and checked for errors and inaccuracies at the end of each trip. Driving patterns for different routes
were developed with the MATLAB program.
3. Analyses of driving characteristics
No. of Routes e 6
Trip distance e 12e26 km
Width of the roadway e 7e24 m
No. of traffic lights e 14e36
Trip duration e 70e120 min
No. of bus stops e 16e24
Collected data were grouped based on time period (morning
peak, off-peak, and evening peak) and road type (7e12 m, 12e18 m
and >18 m). This generated about six groups, and mean values of 14
driving parameters were estimated as shown in Table 2 and Table 3.
The values vary across road type and different time periods.
Running speeds (V2) varied between 20 and 22 km h1 (Table 2).
This indicates that driving characteristics were more or less similar
during peak and off-peak periods. A mismatch between volume of
traffic and road capacity at any point of time has caused such
Data were collected during the morning peak (7:30e9:30 a.m.),
afternoon off-peak (12:30e2:30 p.m.) and evening peak periods
(5:00e7:30 p.m.). The survey was conducted during weekdays and
Table 2
Driving pattern in different time period.
Time
period
V1
(km h1)
V2
(km h1)
Vmax
(km h1)
Acc
(m s2)
Dec
(m s2)
Accmax
(m s2)
Decmax
(m s2)
Pi (%)
Pa (%)
Pd (%)
Pcr (%)
Pc (%)
PKE
(m s2)
No. of
stops/km
Morning
Evening
Off-peak
13.5
11.5
15.0
21.3
19.7
22.5
51.9
49.2
56.8
0.62
0.56
0.67
0.72
0.66
0.73
1.15
1.31
1.09
1.48
1.58
1.29
34.5
37.0
29.3
27.5
25.0
33.0
30.0
28.3
29.0
5.0
8.0
3.8
3.0
1.7
5.0
0.35
0.37
0.34
3.8
3.7
3.3
Table 3
Driving pattern in different road types.
Road type
V1
(km h1)
V2
(km h1)
Vmax
(km h1)
Acc
(m s2)
Dec
(m s2)
Accmax
(m s2)
Decmax
(m s2)
Pi (%)
Pa (%)
Pd (%)
Pcr (%)
Pc (%)
PKE
(m s2)
No. of
stops/km
Type 1 (7e12 m)
Type 2 (12e18 m)
Type 3 (>18 m)
10.1
12.9
16.1
15.4
21.1
26.0
44.5
54.4
57.0
0.49
0.60
0.71
0.58
0.71
0.81
0.95
1.21
1.30
1.15
1.53
1.60
36.7
33.3
32.3
25.3
28.0
30.0
27.3
30.3
29.7
5.3
7.7
4.3
3.0
3.0
3.7
0.45
0.34
0.30
4.0
3.7
3.5
Table 4
Percent number of micro-trips and percent time spent in various average speed ranges.
Average speed (km h1)
v¼0
0 < V < 10
10 < V < 20
20 < V < 30
30 < V < 40
V > 40
Sum
% No. of micro-trips
% of time spent
e
33.1
47.8
13.5
29.3
18.4
13.1
21.1
5.6
8.4
4.2
5.6
100
100
Author's personal copy
(0.3%)
(1.9%)
(3.5%)
(0.4%)
(4.3%)
3.4
3.4
3.4
3.5
3.5
3.6
(0.9%)
(1.0%)
(1.2%)
(2.2%)
(1.4%)
9600
9691
9502
9715
9814
9736
(1.1%)
(3.1%)
(1.4%)
(0.2%)
(0.7%)
0.34
0.34
0.33
0.33
0.34
0.34
(0.7%)
(0.2%)
(2.4%)
(0.5%)
(2.2%)
3.5
3.5
3.5
3.5
3.6
3.6
(0.1%)
(0.3%)
(2.2%)
(0.5%)
(2.4)
4.9
4.9
4.9
4.8
4.9
4.8
(1.1%)
(0.8%)
(1.3%)
(0.6%)
(3.2%)
29.3
29.6
29.5
28.9
29.1
30.2
(0.9%)
(1.2%)
(1.1%)
(2.1%)
(4.5%)
30.1
29.8
29.7
30.4
30.7
28.7
(0.2%)
(0.4%)
(0.7%)
(1.4%)
(1.5%)
32.3
32.2
32.4
32.5
31.8
32.7
(0.4%)
(1.0%)
(1.7%)
(3.1%)
(2.4%)
1.46
1.46
1.47
1.48
1.41
1.49
1.19
1.19
1.19
1.21
1.24
1.14
Values in parenthesis indicate the percent error.
21.8
21.6
22.1
21.5
22.1
21.3
(0.9%)
(1.3%)
(1.4%)
(1.3%)
(2.3%)
54.1
54.2
54.6
53.5
55.4
53.1
(0.2%)
(0.9%)
(1.1%)
(2.4%)
(1.9%)
0.65
0.65
0.66
0.67
0.63
0.64
(0.7%)
(1.7%)
(3.0%)
(3.1%)
(1.6%)
(0.8%)
(3.1%)
(2.2%)
(0.6%)
(2.0%)
0.72
0.71
0.69
0.7
0.72
0.73
(0.5%)
(0.1%)
(1.6%)
(4.1%)
(4.3%)
Dist (m)
PKE (m s2)
Pc (%)
Pcr (%)
Pd (%)
Pa (%)
Pi (%)
Decmax
(m s2)
Accmax
(m s2)
Dec
(m s2)
Acc
(m s2)
Vmax
(km h1)
14.0
14.0
14.2
14.3
13.9
13.8
The second objective of this research is to develop a driving
cycle for intra-city busses in Chennai. There are many methods for
developing a driving cycle based on real-world data such as the
Target
1
2
3
4
5
4. Development of the driving cycle
V2
(km h1)
a situation. The maximum acceleration and deceleration rate
denotes the aggressiveness of the driving behavior. During the
evening peak period, drivers drove more aggressively compared to
other time periods. PKE measures the energy required to accelerate
a vehicle in a given trip. Irrespective of time period, it has a similar
PKE value, which presupposes that the Chennai driving conditions
consume more fuel.
It may be observed from Table 3 that average speed (V1) and
running speed (V2) improve as the width of the road increases. Type
1 roads have lower speed in view of activities such as on-street
parking and roadside shopping. These activities hinder the flow
of traffic and operations. Yet in certain other corridors there is no
divider between different directions of flow. Type 3 roads, on the
other hand, have relatively higher average speed (V1) and running
speed (V2) compared to other roads since they are by and large
wider and straight. Type 3 roads have higher acceleration (Acc) and
deceleration (Dec) rates, which implies that drivers might have
overtaken slow- moving vehicles. Among different road types, the
general
sequence
of
intra-city
bus
operations
are
Pi > Pd > Pa > Pcr > Pc. Higher percentage of time was spent at idle
mode as expected, irrespective of road type. This might have been
due to alighting and boarding of passengers at regular intervals and
fixed delays caused by traffic lights. Creeping (Pcr) takes place
mostly when drivers tend to move from bus stop especially during
peak hours or at intersections where drivers tend to move their
vehicles with small headways. Type 2 roads have a higher
percentage of creeping mainly due to a high number of intersections either controlled manually or by traffic lights.
Trips were divided into micro-trips to analyze in detail. A microtrip is generally defined as sequence of driving characteristics
between two stops (Hung et al., 2005; Tamsanya et al., 2009).
Micro-trips were grouped based on the average speed of each trip
as shown in Table 4. More than 90 percent of intra-city bus trips in
Chennai have an average speed less than 30 km h1. More than 45
percent of time is spent at a speed below 10 km h1, including
idling conditions. It is clearly vouchsafe for the frequent stop-andgo conditions either due to traffic congestion, traffic lights, or for
boarding and alighting of the passengers. This shows that MTC
buses ply for less than 5 percent of time at free flow speed of
40e50 km h1. Such conditions may accentuate vehicular emissions and fuel consumption significantly.
Buses are driven along the same route every day. It is evident
from the previous analysis that operating conditions of buses are
influenced by time spent on different road types in each trip rather
than by time period. Therefore, for this analysis, micro-trips were
regrouped based on the road type. Table 5 illustrates that number of
micro-trips and time spent are higher in type 2 roads. This indicates
that in Chennai, buses ply mostly in type 2 roads followed by type 3
and type 1 respectively. However, the fact that time spent in type 1
roads is higher than that in type 3 roads signifies that the former
has higher traffic density and congestion.
No. of
stop/km
100
100
V1
(km h1)
23.4
19.8
Driving
cycle
56.1
49.5
(0.5%)
(1.2%)
(1.9%)
(0.9%)
(1.6%)
Type 1 (7e12 m) Type 2 (12e18 m) Type 3 (>18 m) Sum
% No. of micro-trips 20.5
% of time spent
30.7
Table 6
Comparison of developed driving cycle with the target of driving parameters.
Road type
Sum of
errors
Table 5
Percent number of micro-trips and percent time spent in various road types.
5473
9.4%
18.4%
26.7%
23.4%
36.2%
K.S. Nesamani, K.P. Subramanian / Atmospheric Environment 45 (2011) 5469e5476
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K.S. Nesamani, K.P. Subramanian / Atmospheric Environment 45 (2011) 5469e5476
Table 7
Comparison of percent number of micro-trips by road type.
35
Road type Type 1 (7e12 m) Type 2 (12e18 m) Type 3 (>18 m) Sum of errors
(1.2%)
(0.7%)
(1.2%)
(1.8%)
(2.3%)
23.4
23.1
23.5
24.5
23.7
22.9
(1.3%)
(0.4%)
(4.7%)
(1.3%)
(2.1%)
4.5%
2.6%
7.9%
6.5%
8.4%
30
25
Frequency (%)
20
15
10
Values in parenthesis indicate the percent error.
5
0
Table 8
Comparison of percent time spent by road type.
30.7
31.6
29.7
30.2
31.1
31.5
(2.9%)
(3.3%)
(1.6%)
(1.3%)
(2.6%)
49.5
48.3
51.1
49.1
49.8
47.9
(2.4%)
(3.2%)
(0.8%)
(0.6%)
(3.2%)
19.8
20.1
19.2
20.7
19.1
20.6
2.5
1.5
0.5
Acceleration
-0.5
(m /s 2)
-1.5
-2.5
Speed (kph)
Road type Type 1 (7e12 m) Type 2 (12e18 m) Type 3 (>18 m) Sum of errors
Target
1
2
3
4
5
60
(2.0%)
(1.5%)
(2.0%)
(3.4%)
(3.9%)
56.1
56.8
55.7
55.4
55.1
57.4
40
20.5
20.1
20.8
20.1
21.2
19.7
20
Target
1
2
3
4
5
0
5474
Fig. 5. Speed, acceleration, frequency distribution of an intra-city bus trip.
(1.5%)
(3.0%)
(4.5%)
(3.5%)
(4.0%)
6.9%
9.5%
7.0%
5.4%
9.9%
Values in parenthesis indicate the percent error.
micro-trip-based method, segment-based method, patternclassification method, and modal-based approach (Dai et al.,
2008). Each method has its own advantages and disadvantages.
The micro-trip based method was selected as in previous studies
(Tong et al., 1999; Tamsanya et al., 2009; Kamble et al., 2009)
because it is simple and flexible for developing the representative
driving cycle.
Buses in general are built with a low power-weight ratio to
improve fuel efficiency and hence it takes long time to accelerate.
Pelkmans et al. (2001) found that the distance-based driving cycle
more accurately reflects reality than the time-based cycle. Therefore, this study has developed a distance-based driving cycle with
a target distance of 9.6 km, which is the average trip length in
Chennai. This should capture all the variations of intra-city bus
driving patterns in Chennai.
Driving patterns in Chennai vary by road type rather than by
time period as mentioned earlier. Therefore, micro-trips based on
road type were used to develop the driving cycle. A computer
program was developed to randomly select the micro-trips until it
reached the target distance. Then the mean value of 14 driving
parameters identified through literature review were calculated for
the combined micro-trips and compared against the target values.
It also calculated the percent number of micro-trips by road type
and percent time spent in each road type to further improve the
representation of observed data. The developed driving cycle was
accepted if the difference was less than 5 percent of the target
value. Otherwise, it was ignored and the whole procedure was
repeated to develop another driving cycle. By repeating this
procedure, about five driving cycles were developed from the
collected data.
Selection of the best driving cycle from among the five developed driving cycles is the next critical step. The relative percentage
differences between the target and the estimated values were
calculated for the 14 driving parameters, number of micro-trips by
road type, and time spent in each road type for this purpose. This is
shown in Tables 6e8. All three factors were equally weighted. The
best driving cycle was chosen based on the least sum of errors
between the estimated parameters and the target values as illustrated in Fig. 4.
SAFD was plotted as shown in Fig. 5. Speed axis ranges from 0 to
50 km h1 with 10 km h1 interval and acceleration axis was
divided at 1 m s2 ranging from 2 to 2 m s2. Frequency axis is in
terms of time spent in different speed-acceleration ranges. SAFD
Fig. 4. Representative drive cycle for Chennai.
Author's personal copy
K.S. Nesamani, K.P. Subramanian / Atmospheric Environment 45 (2011) 5469e5476
5475
70
Speed (kph)
60
50
40
30
20
10
0
Chennai
DBDC
OBDC
Average Speed
CBDC
Running Speed
DUBDC
De Lijn
Maximum Speed
Fig. 6. Comparison of driving speeds among different driving cycles.
Fig. 7. Comparison of time spent in different driving mode among different driving cycles.
has shown that 33 percent of the time is in idle. Two small peaks at
around 20 and 30 km h1 indicate that driving speed in Chennai
tend to be low.
the three international driving cycles are from Pelkmans et al. and
OBDC and DBDC are from ARAI (ARAI, 2007).
The average speed in Chennai is the lowest among different
driving cycles as shown in Fig. 6. DBDC was the closest with
17.7 km h1 whereas the international driving cycle’s speeds were
more than 35 percent higher than that of Chennai’s driving speed.
The comparison of the average running speed is similar to that of
average speed. The only difference is that running speed has
smaller variations, which can be explained by the large proportions
of time spent in idle mode in Chennai. The maximum speeds of
different cycles have wider range between 34 and 61 km h1.
The proportions of time spent in different driving modes vary
significantly from one cycle to another as shown in Fig. 7. Chennai
has the highest proportion of idle time followed by DUBDC with 29
percent. Values from other driving cycles are less, in the range of
6e22 percent, while Chennai has 32 percent. The duration of the
deceleration mode was more or less the same for Indian cycles
5. Comparison with other bus driving cycles
Three well-known international bus driving cycles such as the
Central Business District Cycle (CBDC), Dutch Urban Bus Drive Cycle
(DUBC), and De Lijn were selected apart from Overall Bus Driving
Cycle (OBDC) and Delhi Bus Driving Cycle (DBDC). The CBCD cycle is
used to evaluate transit buses in the United States and Canada. The
DUBC is based on the real-world measurement in urban buses in
several Dutch cities. The De Lijn cycle is extensively used by the
Flemish Public Transport Company to estimate the fuel consumption and exhaust emissions of city buses. OBDC is used in other
cities in India, and DBDC is specifically used in Delhi to measure inuse emissions and fuel consumption. The driving characteristics of
1.0
0.0
e
D
-1.5
Acceleration
Deceleration
PKE
Fig. 8. Comparison of acceleration/deceleration among different driving cycles.
jn
C
D
-1.0
Li
B
U
D
C
D
C
D
B
C
B
O
i
na
n
he
-0.5
C
D
B
D
C
Acceleration (m/s2)
0.5
Author's personal copy
5476
K.S. Nesamani, K.P. Subramanian / Atmospheric Environment 45 (2011) 5469e5476
whereas the international cycles had lower duration. This illustrates that in India, intra-city buses have frequent stop-and-go
conditions. The cruise mode in Chennai is significantly lower than
that of all cycles. For example, in De Lijn cycle the cruise mode is
about 60 percent of the time in a trip whereas in Chennai it is only
about 4.2 percent.
The variation between average acceleration and deceleration rate
is small in the Chennai driving cycle compared to that of international cycles as shown in Fig. 8. This indicates that vehicles frequently
speed up and slow down due to congestion in Chennai. The PKE in
Chennai is higher than that of international driving cycles. This
indicates that the bus drive in Chennai requires 42 percent, 21
percent, and 45 percent more energy than if they were driven using
the CBDC, DUBDC, and De Lijn driving cycles, respectively. Further,
this also indicates the aggressive driving characteristics of intra-city
buses in Chennai, which may contribute to higher exhaust emissions.
6. Conclusions
This study has attempted to analyze the driving characteristics
of intra-city buses in Chennai using 14 driving parameters identified through literature review. It has found that road type and
number of stops in each trip has significant influence on driving
characteristics of intra-city buses. The average speed increases as
width of the road increases. Further, it has revealed that driving
characteristics were more or less similar during peak and off-peak
periods. Irrespective of road type and time period, a higher
percentage of time is spent in idle mode, whereas private vehicles
spent a higher percentage of time in acceleration mode (Nesamani
and Subramanian, 2006). More than 90 percent of intra-city bus
trips in Chennai have an average speed of less than 30 km h1. This
also indicates that intra-city buses in Chennai experience frequent
stop-and-go conditions.
A new intra-city bus driving cycle for Chennai was developed
using the second-by-second GPS data collected from the realworld. This study has developed a distance-based driving cycle to
capture all the variations of intra-city bus driving pattern. The
developed driving cycle was compared against some of the wellknown international driving cycles, which have revealed that it is
not appropriate to adopt a driving cycle of another country for
Chennai. Lin and Niemeier (2003) found that driving cycles may
have similar average speeds and accelerations, but they vary
significantly in terms of frequency and intensity of modal operations from one region to another. Further, this comparison has also
demonstrated that Chennai city has different driving characteristics
than that of Delhi. Hence, driving cycles should be developed for
each city in India.
As a long-term strategy to reduce emissions and to improve fuel
economy from intra-city buses, transit agencies should train their
drivers to adopt eco-driving. Further, traffic engineers should
implement strategies like dedicated bus lanes, (with-flow and
contra-flow bus lanes) and bus priority signals at intersections to
improve the flow of traffic. Intra-city buses need to be improved
both in quantity and in quality to attract personal mode users.
Acknowledgments
The first author would also like to thank the Ford Foundation
International Fellowship Program (IFP) that provided support to
pursue his doctoral study at the University of California, Irvine. The
views expressed in this paper are those of authors and do not
necessarily reflect views of the IFP or the University of California,
Irvine or the Anna University.
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