ESTIMATING TRAFFIC CONGESTION & LOS ON URBAN ROADS USING GPS DATA

Transcription

1 ESTIMATING TRAFFIC CONGESTION & LOS ON URBAN ROADS USING GPS DATA Research symposium, 5 th URBAN MOBILITY INDIA PRESENTED BY ANIL.R, Associate Professor College of Engineering,Trivandrum Dr. M.SATYAKUMAR, Professor, College of Engineering, Trivandrum Shehna Basheer, PG Student,CET

2 INTRODUCTION Traffic congestion critical problem faced by urban area Congestion travel time/delay in excess of that normally incurred under free flow travel conditions Intelligent Transportation System (ITS): Possible solution to urban vehicular congestion ITS: Optimizes use of available road network resources Advanced Traveller Information System(ATIS) Address vehicular congestion problem, provide travelers with accurate pre-trip and en-route road information 2

3 LITERATURE REVIEW Author Title Findings Anjaneyulu, B N Nagaraj (2009) Modelling congestion on urban roads using speed profile data Modeled congestion on urban roads using speed profile data, quantified level of congestion was used as measure of LOS Bhargab Maitra, P K Sikdar (1999) Modelling congestion on urban roads and assessing level of service Unified methodology proposed for the quantification of congestion, incorporating volume and operational characteristics Cesar A Quiroga (2000) Performance measures and data requirements for congestion management systems Presented application of GIS to development of CMS s 3

4 NEED FOR THE STUDY Evaluation of performance measures to monitor traffic system condition relies on accuracy & reliability of collected traffic data Current CMS uses expensive data collection techniques Much of the traffic data have spatial attributes- conventional database systems cannot make use of spatial or locational attributes of data sets Up-to-date congestion data are critical for evaluating improvement strategies that focus on mobility, system reliability & deployment of ITS components 4

5 OBJECTIVES To estimate travel time taken by different modes of vehicles using hand held GPS To derive congestion index of various links in the study stretch To identify the level of service of each link in the study stretch To model the speed of different modes of vehicles during peak hours To develop a graphical user interface through which the user can know about the average speed & travel time of each mode of vehicle at a particular time period 5

6 SCOPE OF STUDY Study was limited to a single corridor in Trivandrum city, ie Sreekaryam- Eastfort Only week days data were considered for analysis Morning peak hour data was used Detailed analysis was done for a single stretch in the study corridor, ie Kesavadasapuram- Pattom stretch 6

7 STUDY AREA Sreekaryam- Eastfort via Ulloor, Kesavadasapuram 7

8 METHODOLOGY DATA COLLECTION Quantifying congestion Identifying LOS at different stretches & Modeling traffic congestion Dissemination of congestion results using GIS Developing User Interface 8

9 1. DATA COLLECTION Multiple runs of GPS probed vehicles at minutes intervals Collected travel time & speed data Data collected by travelling in two wheelers, three wheelers, four wheelers & public transit vehicles Data collection period 8.00 am am (morning peak) Monday - Friday 6.00 am 6.30 am Free Flow Data collected along Kesavadasapuram-Pattom stretch Classified counts of traffic volume Classified counts of parked vehicles on either side of road stretch 9

10 TYPICAL DATA LOG DOWNLOADED FROM GPS UNIT THROUGH MAPSOURCE SOFTWARE 10

11 2. QUANTIFYING CONGESTION Travel time, average speed of journey & congestion index :- major congestion indicators identified Travel time based measures: well understood by professional community & general public as a measure of congestion Congestion is quantified using: Congestion Index Travel Rate Ratio Delay Rate Index 11

12 QUANTIFYING CONGESTION Congestion Index: compare congestion levels in different stretches of road where C= Actual travel time, Cₒ= Free flow travel time CI near 0 - very low level of congestion CI > 2 severely congested conditions 12

13 QUANTIFYING CONGESTION Travel rate ratio Delay rate index Identifies magnitude of the mobility problem in relation to actual conditions (Source: Levinson et al. 2007) 13

14 QUANTIFYING CONGESTION Level Of Service analysis LOS : Qualitative measure for describing operational characteristics in a traffic stream & their perception by drivers & passengers Six LOS designated from A to F (HCM 2010) LOS A -best operating condition (ie free flow condition) LOS F -worst operating condition (ie break down flow) Based on travel rate ratio & Delay Rate Index, LOS of different sections were determined 14

15 LOS based on delay rate index (Source: Levinson et al. 2007) 15

16 3. MODELLING TRAFFIC CONGESTION Mathematical model was developed based on following parameters Speed Operating traffic volume On street parking Kesavadasapuram Pattom stretch was considered Separate models developed for two wheelers, three wheelers, cars & public transit vehicles for morning & evening peak hours Regression equation using SPSS Speed as dependent variable & other parameters as independent ones 16

17 4. DISSEMINATION OF CONGESTION RESULTS USING GIS GIS :- Used as a database for storing & manipulating transportation data Values like LOS, CI, TRR and DRI were entered into the attribute table Maps showing variation of CI and V/C ratio are created 5. DEVELOPING USER INTERFACE A database is created with all the data derived The caption Traveller Information System is given as name of project Coding was done to obtain speed values at different times & different working days during both morning & evening peak 17

18 RESULTS AND DISCUSSIONS Travel time, average travel speed & congestion index are derived from collected probe vehicle data 18

19 CONGESTION INDEX, TRAVEL RATE RATIO & DELAY RATE Road Stretch T.T F.F. T.T Distance Sreekaryam- Ulloor Ulloor- Kesavadasapura m Kesavadasapura m-pattom Pattom- Plammoodu Plammoodu- PMG Congested travel rate=tt/dis tance INDEX Freeflow travel rate=f.f.tt/ Distance TRAVEL RATE RATIO=Con gested travel rate/free flow travel rate DELAY RATE= Congested Travel Rate- Free Flow Travel Rate DELAY RATE INDEX=Del ay Rate/ Congested Travel Rate CI= (Actual TT- Freeflow TT)/ Freeflow TT PMG-Palayam Palayam-Eastfort

20 LOS LOS of various links in South bound direction Sreekaryam- East Fort Road stretch CI Travel rate ratio Delay rate index LOS Sreekaryam- Ulloor F Ulloor- Kesavadasapuram C Kesavadasapuram- Pattom E Pattom- Plammoodu C Plammoodu - PMG D PMG- Palayam C Palayam- Eastfort E 20

21 LOS of various links in North bound direction East fort- Sreekariyam ROAD STRETCH CI TRAVEL RATE RATIO DELAY RATE INDEX LOS Eastfort-Palayam C Palayam-PMG D PMG-Plammoodu B Plammoodu-Pattom C Pattom- Kesavadasapuram C Kesavadasapuram- Ulloor D Ulloor- Sreekaryam E 21

22 TRAFFIC SPEED MODELLING Parameters considered Classified volume counts of vehicles operating on traffic stream No of buses, No: of two wheelers, No: of three wheelers, No: of cars, No: of goods vehicles Classified counts of vehicles parked No: of cars parked, No: of two wheelers parked, No: of three wheelers parked, No: of goods vehicle parked 22

23 ANALYSIS OF MORNING PEAK CONGESTION - KESAVADASAPURAM-PATTOM Speed Modelling of public transits Speed public transits = B C TW A G PC PA PG PTW where A - of auto B - of bus C - of cars TW - of two wheelers G - of goods vehicle PA - of autos parked PG - of goods vehicle parked PC - of parked cars PTW - of parked two wheelers 23

24 SUMMARY OF MODELLED SPEED MORNING PEAK CONGESTION Coefficients Mode Constant of two wheeler of three wheeler of cars of bus of goods vehicle of two wheelers parked of three wheelers parked of cars parked of goods vehicle parked Public transits Car Three wheeler Two wheeler

25 ANALYSIS OF EVENING PEAK CONGESTION - PATTOM-KESAVADASAPURAM Speed of public transits Speed public transits = TW A C B G PTW PA PC PG R 2 =

26 SUMMARY OF MODELLED SPEED EVENING PEAK CONGESTION Coefficients Mode Constant of two wheeler of three wheeler of cars of bus of goods vehicle of two wheelers parked of three wheelers parked of cars parked of goods vehicle parked Public transits Car Three wheeler Two wheeler

27 R 2 value of models developed Modes Two Wheelers Three wheelers Cars Public Transits Morning Peak Evening Peak Models formulated were found to be good for public transits and cars. All the models were validated with field data and satisfactory results were obtained. 27

28 GIS MAPS PREPARED A GIS database is prepared Study stretch is digitized & following details were entered into attribute table Name of links Free flow speed Traffic volume, capacity Travel time taken by different modes of vehicles & their average speed CI and V/C ratio Travel rate ratio and delay rate index LOS 28

29 V/C Ratio of Various Links 29

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32 TRAVELER INFORMATION SYSTEM A database is created with all the data collected Graphical user interface is created in VB User can select the mode of vehicle, time of the day, origin and destination LINK TO TIS 32

33 CONCLUSIONS Estimated congestion indices CI values of links varied from Around 50% links showed CI values >1.5 which showed highly congested stretches Most congested stretch in both directions: Sreekaryam-Ulloor LOS of various links in study stretch is identified LOS varies from C to F 42% of stretches enjoying a LOS C 21% -LOS E; 21% -LOS D 33

34 CONCLUSIONS CONTD A GIS database is prepared Maps showing variation of CI, V/C ratio and average speeds were drawn Helped in better understanding of congested links in study stretch GUI developed 34

35 ACKNOWLEDGEMENT We would like to express our sincere gratitude to Ministry of Urban Development (MOUD), Govt. of India, for their financial support through Centre of Excellence in Urban Transport, Transportation Engineering Division, Dept: of Civil Engineering, IIT Madras,. 35