See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/251665583 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 CITATIONS READS 13 336 2 authors, including: Ks Nesamani California Air Resources Board 9 PUBLICATIONS 133 CITATIONS SEE PROFILE All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. Available from: Ks Nesamani Retrieved on: 30 August 2016 This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright Author's personal copy Atmospheric Environment 45 (2011) 5469e5476 Contents lists available at ScienceDirect Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv 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: firstname.lastname@example.org (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 Author's personal copy 5470 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 Author's personal copy K.S. Nesamani, K.P. Subramanian / Atmospheric Environment 45 (2011) 5469e5476 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), Author's personal copy 5472 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 Author's personal copy 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. 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