Traffic Study of Individual Travel Behaviour in Tiruchirapalli City Using GPSDr.Samson Mathew Assistant Professor Department of Civil Engineering, National Institute of Technology, Tiruchirapalli, Tamil Nadu, India. sams@nitt.edu Y. Vibhakar Reddy Graduate Student Department of Civil Engineering, National Institute of Technology, Tiruchirapalli, Tamil Nadu, India. cva0609@nitt.edu
ABSTRACT
GPS data helps in finding the accurate trip rate information like Travel time, Delay data, Congestion Index, Acceleration Noise, Vehicle speed and Trip ends. In this study, an algorithm is developed for identifying the trip ends of a vehicle by taking dwell time, heading change and speed as the criteria obtained from the GPS unit installed in the vehicle. This trip end data is layered onto the city maps for locating the trip ends and route travelled by the vehicle. Further the Delay, Congestion Index and Acceleration noise for each trip has been calculated. True start and end points of each trip were identified in advance in a trip log completed by participants so that the accuracy of each automated trip division method is measured and compared. A Heuristic model, which combines data collected through GPS and the road network, performs best, correctly identifying 94% of trip ends. 1. INTRODUCTION Infrastructure development is the theme of the day. In India, the economic growth is greatly influenced by rapid development of Infrastructure. Transportation Infrastructure is a vital component not only as part of the Infrastructure but also as an efficient field to boost the growth of it. In this context there is a need to develop innovative methods which can outcome the conventional methods in terms of time taken, amount of labour and accuracy for Transportation demand modeling and transportation planning. Trip rate, the number of trips made by household or an individual in a period of time (usually an hour or a day), is an essential measure in travel behaviour research and transportation demand modeling. It is widely used in planning for trip generation, emission modeling, as well as other related evaluations. Conventional survey methods, such as home interview, roadside interview, mail and phone surveys that collect travel information based on participant recall, are limited, especially in capturing short trips or trip chains. Short trips are frequently omitted by survey participants especially at the end of the day. Congestion analysis, the data for this involve the measurement of accurate speed, time and lot of spatial information where and when the delay has occurred. This data collection also poses similar problems as above. As mentioned above there is a need for fast and effective means of survey methods for Transportation planning. This study is carried out to develop a Geographical Information System in which Global Positioning System is used as a tool, which can automatically do the trip rate analysis and congestion analysis, for the trips along the major roads of Tiruchiraplli City . 2. GPS APPLICATIONS IN TRANSPORTATION ENGINEERING GPS can give real-time reliable data on position, speed of the vehicle in which it is installed, provided the quality of the instrument used. This data obtained from the GPS can be processed and utilized for various studies in Transportation Engineering. The various applications Include Trip Rate Analysis, Travel Time and Delay Studies, Automatic Vehicle Identification, Dynamic Route Guidance System, Advanced Travel Information System, Travel Behaviour analysis. Several studies have been carried out in these areas, Stopher 2003 et.al used dwell time as the parameter to identify the trip ends from the GPS data and identified the trip ends. Jianhe Du and Lisa Aultman-Hall 2007et.al proposed a model which combines dwell time distance and direction change from the GPS data for the successful identification of trip ends. Michael A.P. Taylor , Jeremy E. Woolley, Rocco Zito 2000 et.al carried out congestion studies by integrating GPS and GIS. 3. OBJECTIVES OF THE STUDY
Tiruchirapalli, situated on the banks of the river Cauvery is the fourth largest city in Tamil Nadu, It has a population of 7, 46,062 according to 2001 census. Tiruchirapalli is well connected by road network, it is connected by National Highways NH-45, NH-65 and NH-210. Tiruchirapalli is the hub of Southern Railway's operation to connect this central part of Tamil Nadu to various parts of India, notably regions in Kerala, Andhra Pradesh, West Bengal, Maharashtra, Karnataka, Delhi, and Madhya Pradesh. Tiruchirapalli has an international airport about seven kilometers from the city, which operates flights to Indian cities, territories, and neighbouring countries including Malaysia, Sri Lanka, Singapore, and the Gulf. 5. METHODOLOGY Four major steps involved in the project are
This study focuses on finding the method for processing the large volumes of GPS data and uses it for travel behavior analysis. The method used is passive data collection from GPS; GPS is installed in a probe vehicle, which is a taxi cab and it requires no intervention from the driver it automatically records the spatial data and it is collected later for data analysis. The in-vehicle manual logs are also provided to the driver to compare the results that are obtained from the trip identification method. Equipments and Software Used The equipments used for carrying out GPS survey are:
The HI -305 III GPS receiver is a very smart hinge design, can folded from 00 to 1800 accommodate the best satellite receiving angle. We can adjust the HI -305 III in best angle with our PDA so that the external antenna will not be needed. We can fix the device with receiver in the dashboard of the car. Also, we don't need to face the mobile device display to the sky for better receiving angle. The HP- iPAQ is a pocket PC used for loading the necessary data. With DGPS collections even sub-meter accuracies can also be achieved Fig.1 shows the Pocket PC connected with GPS receiver.  Fig. 1. HI -305 III GPS Receiver with Pocket PC attached DATA PROCESSING The GPS data will give continuous Latitude, Longitude, Time and several other data for every two second interval. The data is stored in Track log data file, this file is processed using the Microsoft Excel for further analysis. The processing is explained in the following steps. Distance Measurement The distance between the two consecutive Latitude Longitude points is obtained by using the formula: d = R arccos(sin(j1) * sin(j2) + cos(j1) * cos(j2) * cos(l1-l2)) Where R is the radius of the earth and d is the distance (l1, j1) and (12, j2) are the two consecutive latitudes and longitudes This formula can be given in Microsoft Excel as shown below, for getting the distance between the two consecutive GPS points, where the first columns of the sheet must be latitudes and longitudes. The distance in miles is converted into meters by multiplying with 1609.44. After this the cumulative distance is calculated in meters. Distance=(ACOS(((SIN(A1*PI()/180))*(SIN(A2*PI()/180)))+((COS(A1*PI()/180))*(COS (A2*PI()/180))*(COS((ABS(B2-B1))*PI()/180))))*180/PI())*69 Identification of Trip Ends A potential trip end is flagged off by following the two criteria which from the research of Stopher et al. (2003)
 Fig. 2. Plotting of Trip End on Google Earth Imagery Congestion From the processed data the following parameters are calculated for congestion analysis. Delay: A commonly accepted definition of delay is system delay (d) defined as the excess travel time above the minimum (free flow) travel time to traverse a network element. If T is actual time T0 is the free flow time, then the system delay is D= T- T 0 Congestion Index: The level of delay as defined by the system delay can be expressed in terms of a dimensionless Congestion Index (CI), as described by Richardson and Taylor (1978). CI = (T- T 0) / T 0 Acceleration Noise: Acceleration Noise (AN) is a parameter derived from acceleration pattern contained within speed-time profile for the journey and provides a measure of the quality of traffic flow and thus of the level of congestion. AN is given by the equation:  Where T = Total time Δvi = Change in the velocity Δti = Change in time 5. ANALYSIS AND RESULTS Trip Rate Analysis Trip ends are identified by processing the data as mentioned in the previous discussion. The GPS data collected for one week from the cab is processed and the trip ends for all the days are identified. The starting and ending time of the trips and distance travelled are also calculated, for example trip for a day are shown in Table 1 and these are presented on the maps using Google –Earth as shown in Fig. 3. Table 1. Trip details for the day 26 -10-07
Discussion Identification of Trips
Fig. 3. Trip details for the day 26 -10-07 on Google - Earth Comparison of Trip ends To verify the results obtained from the method used for identification of trip ends from GPS data and manual trip ends entered by the driver of the probe vehicle in the data log sheet provided with him. It is observed that on most of the days of the survey, the trip ends obtained from the algorithm are more than the manual trip ends. This indicates the trip reporting negligence of the driver, small trips are not entered into the data sheets by them. On 22-11-07 trip ends identified by the algorithm are less than those entered manually, because of the data loss in GPS data collection. Congestion Analysis The major trips identified in Trip rate analysis during the one week survey are considered for the congestion analysis. The congestion parameters Delay, Congestion Index, Acceleration are calculated for the above nine trips which are obtained from the probe vehicle. These results are consolidated and shown in Table 2 below Discussion Delays Delays occurred were mainly due to the stops at the signaled intersections except on 23 -11-07. Signaled delays occurred at the following intersections
Speed The average speed of the probe vehicle for the trips except on 23-11-07 has been 28 Kmph. On 23-11-07 it is very low due to more number of congestion and signal delays, all the trips occurred on that day have very low speeds on NH-45 stretch between Palpanni to Toll gate. Hence it is observed that the highway stretch between Palpannai to Toll Gate is accounting for traffic congestion. This is due to the construction work going on the stretch and also due to the large volume of traffic on this road. 6. CONCLUSION In this study the need for automatic data collection system for transportation demand modeling and planning is discussed. The advantages of the Global Positioning for collecting continuous data required for transportation modeling and planning are utilized, it is observed that GPS gives more accurate and reliable data for trip rate analysis than the conventional methods like trip reporting by the drivers. The short trips that are not listed by the driver are identified by the method used for trip end identification using GPS. In this study it is observed that from the continuous speed and time data obtained from the GPS data, the travel behavior of the probe vehicle is studied and routes which have high congestion during the survey are identified. The congestion analysis has been carried out for the selected trips from the one week survey which occurred on main arterials of Tiruchirapalli city. REFERENCES
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