Short Paper Proc. of Int. Conf. on Recent Trends in Transportation, Environmental and Civil Engineering 2011
GIS Application in Traffic Congestion Management Mr. R. Chandra prathap1, Mr. A. Mohan Rao2, Dr. B. Kanaga Durai3, Dr. S. Lakshmi4 1
ME Student Division of Transportation Engineering, Anna University Chennai, India email@example.com 2 Scientist E1, RDM Division, Central Road Research Institute, New Delhi, India. firstname.lastname@example.org, email@example.com
Abstract—The urbanization in developing countries indicates that more people live in cities than before. The trend of urbanization, population increase and the increase in number of registered vehicles induces pressure on traffic movements and makes living in urban area more difficult. General congestion related data collection and congestion management measure is labour intensive and a heavy investment is needed for these mitigation measures. Hence to make this work feasible, latest technologies like GIS and GPS wisll help to analyze the live traffic situation and suggest the cost effective measures to mitigate the congestion, an attempt was made to use GPS and GIS effectively for data collecting, data analysing and result displaying process.
To estimate the congestion using the model developed. To use GIS as a tool for effective congestion dissemination and management. D. Overview of Remaining Sections This paper is organized as follows. Section 2 reviews the earlier work that was performed regarding congestion modeling and the various works that were done using GPS and GIS for congestion management. Section 3 provides a brief methodology of the proposed work and the description of study area. Section 4 discuss about the various data collection techniques and the data that have been collected for the study. Section 5 briefs about the road section data analysis which was carried out in GIS environment. Section 6 and 7 discuss about the traffic speed modelling work and validation of the developed model. Section 8 is structured particularly to expose the GIS application in traffic studies and Section 9 provides an overall conclusion of the work with future scope of study.
Index Terms—GIS Application, Congestion Management, Traffic speed, Modeling, Regression Analysis.
I. INTRODUCTION A. General A transportation system should satisfy the perceived social and economic needs of the user, as the need changes transportation system itself evolves and problems occur if it become inadequate. The critical problem that an urban area faces is the traffic congestion which occurs when the demand exceeds the capacity. Main cause of congestion is oversaturation and the situation worsens if an incident occurs. General congestion management measures include a wide range of data collection, system monitoring, identifying and evaluating transportation control measures. These types of measures and management can be done effectively with the help of latest advancements in GIS and GPS.
The literatures review was done to find the various key parameters of congestion, existing methodologies that were adopted for congestion modelling and the existing GIS application in the area of traffic congestion management. III.
METHODOLOGY AND STUDY AREA
The methodology adopted for the study is: Identification of parameters which affect traffic congestion. Selection of study corridor. Collection of data. Identification of suitable model. Modeling traffic speed. Model validation. Estimation of congestion using model. Dissemination of congestion results using GIS. The subsequent Para explains the proposed methodology in brief. The various parameters identified which effect traffic congestion include traffic characteristic such as speed, travel time and percentage compositions of all modes of vehicles and roadway characteristics which includes number of lanes, turning radius, friction points and number of flyovers. For the current study two heavily congested urban corridors one from Chennai and another from Delhi are selected. The study area selected in Chennai is NH45 which is one of the major arterial road links which connect Chennai city with southern part of Tamilnadu. This road is mostly congested and reaches jam condition especially during peak hours. The another
B. Need For Study The existing system of congestion management system requires more reliable data which is expensive to collect and maintain. Hence advancements in data collection and presentation techniques such as Global Positioning System (GPS) and Geographic Information System (GPS) if properly used will enhance a good traffic congestion management system. C. StudyObjectives The objectives of this study are: To determine the various factors that influences traffic congestion. To develop a model for prediction traffic congestion. 3 Scientist F & Hea d, RDM Division, Central Road Research Institute, New Delhi, India. 4 Professor & Head Division of Transportation Engineering, Anna University Chennai, India.
© 2011 ACEE DOI: 02.TECE.2011.01.4
Short Paper Proc. of Int. Conf. on Recent Trends in Transportation, Environmental and Civil Engineering 2011 of data so as to make the analysis easier. For this purpose a Map basic program was developed in MapInfo software so as to make the work easier. The developed program was used for preliminary analysis for all sections thus extracting each reference distance, section travel time and averages of other sectional parameters.
corridor selected for study is Delhi Inner Ring Road which is around 50 km long circular road that encircles important locations of the city and is in fact one of the longest road in Delhi. The Selected corridors are shown in Figure1 and Figure2.
B. Road Segmentation MapInfo Software is used for the segmentation of road network. Inner ring road consists of around 38 major intersections of which most of them are controlled by constructing flyovers and some of them by signals. Ring road was divided so that each segment encloses a flyover. Totally 20 sections were identified in Inner ring road and 6 sections were identified at Chennai. VI. TRAFFIC SPEED MODELLING
Figure 1. Study area in Delhi-Inner Ring Road.
The basic reason behind the traffic speed modelling is to examine how the traffic speed fluctuates with respect to the other traffic and roadway characteristics. Traffic speed was modelled by considering other traffic characteristics such as travel time, percentage composition of vehicles and roadway characteristics such as number of lanes, turning radius, friction points and number of flyovers. Statistical analysis was carried out using Statistical Package for the Social Sciences (SPSS) software to develop a linear regression equation taking speed as a dependent variable and other related characteristics as independent variable. Figure 2. Study area in Chennai-Anna Salai.
A. Factor Analysis A factor analysis is a statistical method which is utilized to discover factors among observed set of variables and it is used mostly for data reduction purposes. If a data set contains an overwhelming number of variables a factor analysis may be performed to reduce the number of variables for analysis. Factor analysis was performed by considering fourteen variables i.e., speed, time, mode, centre of deviation, number of flyovers, friction points, radius of turn, percentage composition of two wheeler, buses, trucks, LCV’s, SMV’s, cars and autos respectively. Totally five factors were obtained of which factor one comprises variables such as mode, percentage composition of two wheeler, trucks, SMV’s and cars respectively, factor two comprises of three variables such as percentage composition of autos, friction points and radius of turn respectively, third factor includes percentage composition of LCV’s and number of flyovers, fourth factor includes travel time and percentage composition of buses, and the fifth factor consists of centre of deviation. In this percentage composition of vehicles were loaded in first, second, third and fourth factor whereas roadway characteristics such as friction points, number of flyovers and radius of turn were contributed in second and third factor. Driving characteristics such as time and centre of deviation were contributed in fourth and fifth factor. The component matrix for Delhi data is given in Table 1.
A. Travel Time Data Travel Time is the important data that is needed for the proposed work. Travel Time includes both running time and stopped time of the vehicle. Manual method of data collection is time consuming and more prone to errors. Hence, GPS device is used for data collection which is capable of providing a highly accurate, continuous global position data such as latitude, longitude, altitude and time. VBOX 3i GPS Device is used for data collection in Delhi. For Inner Ring Road, many runs were made by floating car method both in the Clockwise and Anticlockwise direction at different time period of a day. GPS data for the Chennai corridor was collected using HAICOM GPS Device. In Chennai, data were collected for two working days and one non working day and the survey is conducted during morning, evening and afternoon peak hours. Other than this volume count data and friction point data were collected from secondary sources. V. SECTIONAL DATA ANALYSIS A. Data Segregation Travel time data collected is a continuous one i.e., starting from a point and ending at the same starting point. Since enormous amount of data are recorded it is difficult to do analysis as a whole. Hence it is necessary to do segregation © 2011 ACEE DOI: 02.TECE.2011.01. 4
Short Paper Proc. of Int. Conf. on Recent Trends in Transportation, Environmental and Civil Engineering 2011 Speed(ACWD)= –1.712 + 0.0601* rad_of_tur + 0.585*buses – 0.000344* Centre of dev + 0.245* Friction points + 2.246* No_of_flyovers + 0.087* Two Wheeler – 0.039*cars – 0.013*smvs (2)
TABLE I. COMPONENT MATRIX SHOWING VARIABLE DISTRIBUTION
A model was also developed to check whether the total volume of traffic in the road segment makes a significant change in the R square value of equation 6.2. Speed(ACWD)= 6.095 + 0.0601* rad_of_tur + 0.453* buses – 0.000304* Centre of dev + 0.240* Friction points + 1.778* No_of_flyovers + 0.0049* Two Wheeler – 0.098*cars – 0.122*smvs – 0.000013*total volume (3) R square value by considering total volume is 0.918 which shows that there is a least significant change in the (3). Similarly for Chennai study stretch,
Although the percentage variance contributed by all the five factors is 75, variables with similar characteristics are distributed in two or three factors and hence it is difficult to group the factors based on the common thread among the variables and therefore it is necessary to move on to stepwise regression analysis with these variables for developing the model.
Speed = -149.285 + 2.721* Two Wheeler + 0.479*cars + 6.364*buses + 1.025*trucks + 5.275* Friction points – 2.690*travel time
B. Stepwise Regression Analysis Stepwise regression is a procedure that relies on a userselected criterion, such as R-squared, adjusted R-squared, F-ratio and other related measures, to select a best model among competing models generated by the procedure. Stepwise regression model was developed with speed as dependent variable and all other parameter (mode, friction points, radius of turn, centre of deviation, number of flyovers, time, percentage composition of two wheeler, trucks, SMVs, cars, autos, buses and LCVs) as independent variable. The developed model is shown below in equation 6.1.
VII. MODEL VALIDATION It is necessary to validate the model developed by verifying that the model is able to reproduce observed traffic movement to a level appropriate for its use. The model developed for Delhi study area was validated by considering the following criteria. Criteria 1: Applying the developed model for the same study area of city. Criteria 2: Applying the developed model for different road network of city. Statistical tests such as T-test and F-tests were carried out to asses weather the mean and variance of actual and modelled data has significant difference or not. The test result shows that the modelled speed data deviates a least from the actual speed data. The hypothesis test result shows that P-value is not less than 0.0001, and the conclusion is that the difference between the two means is statistically insignificant for all cases. Hence the modelled developed is performing well.
Speed(ACWD)= -5.283 + .060*rad_of_tur + .642*Buses + .0003*Centre of dev + .244*Friction points + 2.273*No_of_flyovers + 0.135*Two Wheeler (1) The model given in (1) comprises of parameters such as radius of turn, centre of deviation, friction points, number of flyovers, percentage composition of two wheeler and buses. The R square value of the model is 0.917 which is good. However, the percentage composition data collected from Delhi inner ring road indicates that cars and SMVs contribute a high and considerable amount of volume to the traffic. But in the developed model cars and SMVs are eliminated during regression analysis. Hence it is mandatory to include percentage composition of cars and SMVs so as to clearly represent the realistic traffic condition in the model. Hence along with other parameters cars and SMVs percentage composition were added and the model is redeveloped as shown in (2)
© 2011 ACEE DOI: 02.TECE.2011.01. 4
VIII. GIS APPLICATION A geographic information system (GIS) is a computerbased tool for mapping and analyzing geographic phenomenon that exist, and events that occur, on Earth. GIS technology integrates common database operations such as query and statistical analysis with the unique visualization and geographic analysis benefits offered by maps. A. Database Development Database preparation and development plays a major role in any GIS based activities. The main target of a database development is to provide information in GIS background in a user friendly manner. It involves a sequence of steps to 68
Short Paper Proc. of Int. Conf. on Recent Trends in Transportation, Environmental and Civil Engineering 2011 attain the described target. The data collected from GPS devices includes latitude, longitude, speed, number of satellite, etc., are transferred into a Personal Computer (PC) in a text file(*.txt) format. The text files are then converted in to excel file format by using GPS visualizer software for further sectional analysis. The analyzed data cannot be directly linked in GIS environment hence it should be converted in to database file format for linking it to GIS field. B. Actual Speed Vs Modelled Speed
Figure 6. Comparison of Working and Non-working Day Speed – Anna Salai Chennai.
Figure 5 displays the mode wise speed comparison data for inner ring road, Delhi. Figure 6 shows the speed comparison of working and non working day for Anna salai, Chennai. It is easily inferred that working day speed is comparatively less when compared to non-working day speed. This is because in general most of the transportation oriented activities such as official trips, school trips and other commercial trips will occur more on working days. That to during the peak hour these trips will be more say, official and school trips will be more in morning and evening peak periods.
Figure 3. Comparison of Actual Speed and Modeled Speed – Inner Ring Road Delhi.
8.2.2 Congested Section (Speed < 15 Kmph)
Figure 4. Comparison of /actual speed and Modeled speed-Anna Salai Chennai.
Figure 3 depicts the actual and modelled speed data comparison for the downward traffic flow of Delhi inner ring road. The figure explains that the actual and modelled speed data are almost coincides in majority of ring road sections except a few. It can be observed that in sections 15 and 16 both the actual and modelled speed is exactly the same as one other Similarly, Figure 4 disseminates the actual and modelled speed comparison for the downward directional traffic flow movement of Chennai study stretch. The picture clearly shows that the model developed predicts the speed for all the segments in an accurate way.
Figure 8. Congested Section – Inner Ring Road Delhi.
B. Speed Comparison
Figure 8. Congested Section – Anna Salai Chennai.
Figure 7 and 8 shows the congested section in Delhi and Chennai study stretches. When we compare above two figures Chennai has much more congested points when compare to Delhi. This is because in Chennai frequent signals are observed all along the length of the road however in Delhi most of the signals are jumped out by constructing flyovers. Figure5. Comparison of Mode wise Speeds – IRR Delhi.
© 2011 ACEE DOI: 02.TECE.2011.01. 4
Short Paper Proc. of Int. Conf. on Recent Trends in Transportation, Environmental and Civil Engineering 2011 CONCLUSIONS
The significant results obtained from this study are The average speed of bus (28Kmph) moving in downward direction of Delhi inner ring road is less when compared with two wheeler (37Kmph), three wheeler (35Kmph) and car (42Kmph) and the trend is same for the other direction. The average speed of bus (22Kmph), three wheeler (33Kmph) and car (36Kmph) moving in upward direction of Inner Ring Road is lesser than those moving in downward direction of the same road which clearly shows that these downward direction traffic is suffering from congestion when compare with the later. In Chennai, during working days the average speed of traffic flowing towards city was found to be 20Kmph which was lesser compared to the traffic flowing from city (22Kmph). This clearly shows that during working days more people migrate towards city for official and business purpose. For the aforesaid case during holidays the situation takes place in a reverse manner i.e., the average speed of traffic flow towards city (32Kmph) is more when compared to that flows away from the city (29Kmph). This may be because more people travel away from city for recreational purposes. In this study GPS and GIS was used for input data preparation for model development. It was used to disseminate congestion results and for modal speed comparison. It was used as a cartographic tool to display results however, the effective use of GIS was made during data analysis. This paper concentrate more on the methodology and due to time consideration the execution of the methodology is limited. The transferability of the models developed was not fully tested in this study because the data collection technologies are different for different cities; however one can try with same technology for the study area and can work/test for transferability of models developed.
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ACKNOWLEDGMENT The authors are indebted to the help rendered by the field staff in data collection. The authors wish to thank Dr. S. Gangopadhyay, Director, Central Road Research Institute, New Delhi, India for his motivation, guidance, suggestions and kind approval to publish this paper.
© 2011 ACEE DOI: 02.TECE.2011.01. 4