A multi-objective transit trip itinerary planning system using gis for bangalore city

1. A multi-objective transit trip itinerary planning system using gis for bangalore city By Dr. Ashish VermaAssistant Professor (Dept. of Civil Engg.) and Associate Faculty (CiSTUP)Indian Institute of Science (IISc) Bangalore – 560012, IndiaE-mail: ashishv@civil.iisc.ernet.in Presentation at Geo-Infra 2012, Gurgaon 9th Feb. 2012

2. Overview • Introduction • Problem Definition • System Architecture and Design • Case Study • Conclusion Dr. Ashish Verma, IISc Bangalore

3. Dr. Ashish Verma, IISc Bangalore

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5. Dr. Ashish Verma, IISc Bangalore

6. Introduction • Public transport modes are considered as efficient modes of transport because they carry more number of passengers per unit of space occupied. • Due to spatial and temporal variations in transit services, roadway, traffic and weather conditions, travelers within cities are not generally conversant with the prevailing public transport schedules, routes and traffic conditions. Dr. Ashish Verma, IISc Bangalore

7. Passenger Information Systems • Passenger Information Systems aim at bridging the information gap by providing the pre-trip and/or en-route information to the travelers about their travel options, so that they can take well informed travel decisions. Dr. Ashish Verma, IISc Bangalore

8. PASSENGER INFORMATION SYSTEMS Passenger Information System (PIS) Assist travelers by providing them timely, accurate, reliable and relevant information Influence their travel behavior about mode choice, time of travel, cost of travel, route choice or trip making. Dr. Ashish Verma, IISc Bangalore

9. Good Passenger Information System Dr. Ashish Verma, IISc Bangalore

10. Good Passenger Information System Dr. Ashish Verma, IISc Bangalore

11. PASSENGER INFORMATION SYSTEMS Trip Planning System Assist in pre-trip planning through a stand-alone kiosk or a web-site. Assist in identifying the shortest path along the public transport network, to perform a trip from user’s origin to destination. Displays the complete graphical and textual directions to perform the trip. Dr. Ashish Verma, IISc Bangalore

12. Geographical Information Systems • Geographical Information Systems (GIS) are a powerful tool for visualization, planning, operations, analysis, and maintenance across many areas and functions. • Because of the spatial nature of transportation data, GIS is the perfect match. Using a base map and layered, geographically coded data, GIS provide a variety of spatial views and broad query capability. Dr. Ashish Verma, IISc Bangalore

13. Trip Planning System Successful examples Multi-modal transit system in Berlin, Germany (Operator-BVG). Bay Area Rapid Transit (BART), San Francisco, USA. Dr. Ashish Verma, IISc Bangalore

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15. Dr. Ashish Verma, IISc Bangalore

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17. Components of a Trip Origin Walking Travel Waiting Transfer Travel Walking Dr. Ashish Verma, IISc Bangalore Destination

18. Generalized Cost as a Parameter • An assumption that users are simply aiming to minimize their money costs, when in fact they attach great significance to time saving or vice versa, may lead to errors in trip planning. • For this reason, most of the urban transport models use some form of GC (as a impedance parameter) rather than simple "out of pocket" money outlays. • Hence, it is felt that this concept of GC can be potentially adopted in Passenger Information Systems also. Dr. Ashish Verma, IISc Bangalore

19. Generalized Cost as a Parameter • This consideration is especially important in the Indian scenario, where the various modes of transport are generally not integrated, and the transfer time from one mode to another may be very large. • Also, many Indian cities follow experience or thumb rules more than some scientific approach, to decide routes and schedules of their public transport systems. This may lead to higher and uncomfortable walking and waiting times for the user. • Besides all these, a major share of public transport users in India are captive riders, for whom the fare paid for making a trip is also an important criteria for choosing any transit route for trip making. Dr. Ashish Verma, IISc Bangalore

20. Complexities of Transit Network Transit service is time dependant. The availability and the level of transit service vary by time of day and day of week. For the same route, not every segment of the route has the same amount of services all the time. Some stops lack service during some times of the day and some days of the week. (peak/off-peak, express/limited, weekday/weekend services). (Figure) Hence, integration of transit routes and temporal transit services is necessary. Dr. Ashish Verma, IISc Bangalore

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22. Complexities of Transit Network One bus stop may serve multiple routes, and more than one transit route may share the same street links. Hence, trip planning system requires a relational database linking bus stops, transit route, and street network. Not every bus stop has a scheduled arrival and departure time (time point). Hence, for trip planning the location of buses at specific times has to be estimated. These unique features make the minimal path finding application for transit networks much more challenging. Dr. Ashish Verma, IISc Bangalore

23. Problem Definition • Develop a web-based Transit Passenger Information System, which takes into account: • Generalized Cost approach • Multi-objective user queries • Time of arrival and departure • Maximum number of modal transfers • Maximum walking distance Along with one of the basic choices: • Optimum path based on generalized cost • Minimum time • Minimum cost Dr. Ashish Verma, IISc Bangalore

24. SYSTEM ARCHITECTURE AND DESIGN Dr. Ashish Verma, IISc Bangalore

25. SYSTEM ARCHITECTURE AND DESIGN Dr. Ashish Verma, IISc Bangalore 25

26. USER INTERFACE User Query Itinerary planning General Information • Bus time table • Metro time table Enter the maximum number of modal transfer Display of Results • User Query for trip based on • Shortest distance • optimum time • optimum cost • optimum generalized cost Module for finding optimum path based on user query Enter the Origin, Destination(s), Day and time of travel Dr. Ashish Verma, IISc Bangalore

27. Network Analysis Steps followed: • All transit nodes that are within walking distance of the user’s trip origin and destination are found and flagged. • Travel time calculation: • In-vehicle time • Walking time • Waiting time • Transfer time Dr. Ashish Verma, IISc Bangalore

28. Network Analysis • Generalized Cost Calculation • Cost-per-unit-time’s for the four time segments are calculated on the basis of the weightages input by the user. • Algebraic sum of fare and monetary equivalents of the time segments is assigned to each possible path. Dr. Ashish Verma, IISc Bangalore

29. MATHEMATICAL FORMULATION Objective Function: Min Z = w1twko,d+ w2twto,d+  (w3ttri,j.r + ci,j.r)xi,j + w4 ttti,jxi,j if arc is used, = {0} otherwise Dr. Ashish Verma, IISc Bangalore 29

30. Case Study and Implementation • City of Bangalore • Public transport • BUS • Metro • Bus – BMTC Metro – BMRCL • The first stretch between Baiyyappanahalli and M.G. Road was inaugurated on October 20, 2011. • During the first month, since the opening of Reach I, about 13.25 lakh people have taken ride on the metro. The BMRCL has earned a revenue of Rs 2.1 crore (US$462,000) in its first month of operation Dr. Ashish Verma, IISc Bangalore

31. Case Study and Implementation • The proposed system architecture for the Transit PIS is implemented in a commercially available GIS software TransCAD and its associated programming language GISDK. • The objective is to test the methodological framework on the real city network of the study area, Bangalore city. Dr. Ashish Verma, IISc Bangalore

32. Data requirement • Road network of Bangalore – digitized • Bus stops in Bangalore – digitized and field collection • Bus routes – digitized • Stage information for fare calculation – Form 4 BMTC / Metro document • Schedule information for planning – Form 3 BMTC / Metro document • Dwell time at bus stops – modeled • Travel time of buses for arrival time prediction – modeled Dr. Ashish Verma, IISc Bangalore

33. Field survey • Road information • Divided/undivided Carriageway, road width, shoulder width, footpath width, road name, no. of lanes, one-way/two-way • Bus stops information • Latitude, longitude, name Dr. Ashish Verma, IISc Bangalore

34. Digitization • Road digitization using Google maps and TransCAD • Stops digitization based on field data • Routes based on information from BMTC Dr. Ashish Verma, IISc Bangalore

35. Roads digitization Dr. Ashish Verma, IISc Bangalore 35

36. Bus stops digitization 36 Dr. Ashish Verma, IISc Bangalore

37. Routes map 61 Dr. Ashish Verma, IISc Bangalore

38. Data entry to build database • Form 4 data (stages information) • Form 3 data (schedule information) • BMTC website for fare structure • Road information (field data) Dr. Ashish Verma, IISc Bangalore

39. Table: Transit/non-transit modes

40. Table: Snapshot of Mode table

41. Travel time modelling • Need to know the arrival time of the bus at each bus stop • Due to the bus sharing space with other traffic, travel time variability is greater • Travel time depends upon: traffic flow, passenger demand, intersections, frequency of stops, etc. • Travel time = dwell time + link travel time Dr. Ashish Verma, IISc Bangalore

42. Dwell time modelling • Definition: time the bus stops at a loading area to serve the passengers • Factors affecting dwell time: passengers alighting, passengers boarding, total number of passengers, payment type. • Possible other factors: day of week, time of day, type of route. Dr. Ashish Verma, IISc Bangalore

43. Travel time model - MLR Dr. Ashish Verma, IISc Bangalore

44. Travel time modeling • An attempt was made to develop travel time model considering • distance between stops, • number of intersections between stops, • passengers boarding/alighting at stops, • dwell time at each stop, • total number of passengers on board, • type of bus, • time of day, • day of week. • 1200 observations were considered • The obtained model had a poor r square value of 0.007 Dr. Ashish Verma, IISc Bangalore

45. Travel time modeling • Since the obtained model was poor, in the current work weighted averages of speeds were calculated for different category of buses and the same were used in the model. Dr. Ashish Verma, IISc Bangalore

46. Trip Planner - Walkthrough 71 Dr. Ashish Verma, IISc Bangalore

47. Trip Planner - Walkthrough Dr. Ashish Verma, IISc Bangalore 71

48. Trip Planner - Walkthrough Dr. Ashish Verma, IISc Bangalore 71

49. Trip Planner - Walkthrough Dr. Ashish Verma, IISc Bangalore 71

50. Trip Planner - Walkthrough Dr. Ashish Verma, IISc Bangalore 71