A multi-objective transit trip itinerary planning system using gis for bangalore city By Dr. Ashish Verma Assistant Professor (Dept. of Civil Engg.) and Associate Faculty (CiSTUP) Indian Institute of Science (IISc) Bangalore – , India Presentation at Geo-Infra 2012, Gurgaon 9 th Feb. 2012

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

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6 I NTRODUCTION 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.

7 Dr. Ashish Verma, IISc Bangalore P ASSENGER I NFORMATION S YSTEMS 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.

8 Dr. Ashish Verma, IISc Bangalore 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.

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

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

11 Dr. Ashish Verma, IISc Bangalore 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.

12 Dr. Ashish Verma, IISc Bangalore G EOGRAPHICAL I NFORMATION S YSTEMS 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.

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

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

18 Dr. Ashish Verma, IISc Bangalore G ENERALIZED C OST AS A P ARAMETER 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.

19 Dr. Ashish Verma, IISc Bangalore G ENERALIZED C OST AS A P ARAMETER 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.

20 Dr. Ashish Verma, IISc Bangalore 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.

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22 Dr. Ashish Verma, IISc Bangalore 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.

23 Dr. Ashish Verma, IISc Bangalore P ROBLEM D EFINITION 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

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

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

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

27 Dr. Ashish Verma, IISc Bangalore N ETWORK A NALYSIS 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

28 Dr. Ashish Verma, IISc Bangalore N ETWORK A NALYSIS 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.

29 Dr. Ashish Verma, IISc Bangalore Objective Function: Min Z = w 1 t wk o,d + w 2 t wt o,d +  ( w 3 t tr i,j.r + c i,j.r )x i,j + w 4  t tt i,j x i,j 29 MATHEMATICAL FORMULATION if arc is used, = {0} otherwise

30 Dr. Ashish Verma, IISc Bangalore C ASE S TUDY AND I MPLEMENTATION 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, During the first month, since the opening of Reach I, about 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 A quick overview of BMTC Every Day Traffic Revenue Rs.3.75 Crores No of Schedules5901 No of Vehicles6140 Daily Service Kms12.57 Lakhs No of trips79001 No of buses under PPP17 Depots37 Bus Stations48 Staff employed32680 Daily Passengers Carried4.5 million

31 Dr. Ashish Verma, IISc Bangalore C ASE S TUDY AND I MPLEMENTATION 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 D ATA 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

33 F IELD 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 D IGITIZATION 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 R OADS DIGITIZATION Dr. Ashish Verma, IISc Bangalore

36 B US STOPS DIGITIZATION Dr. Ashish Verma, IISc Bangalore

61 R OUTES MAP Dr. Ashish Verma, IISc Bangalore

38 D ATA 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

Table: Transit/non-transit modes Mode ID Description 1Metro 2Walk access/egress/transfer 3Vayu Vajra Airport service 4Vajra 5Big 10 6Suvarna 7Pushpak 8Special services 9Ordinary services 10Other services

Table: Snapshot of Mode table Mode_NameMode_IDMode_usedImpedence_fieldFare_typeFare_matrix Metro11Metro_TT2Fare_Metro Walk21Walk_TT1 Vayu Vajra Airport service31BMTC_TT1Fare_BIAL Vajra41BMTC_TT1Fare_Vajra Big 1051BMTC_TT1Fare_Big_10 Suvarna60BMTC_TT1Fare_Suvarna Pushpak70BMTC_TT1Fare_Pushpak Special services80BMTC_TT1Fare_Special Ordinary services90BMTC_TT1Fare_Ordinary

41 T RAVEL 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 D WELL 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 T RAVEL TIME MODEL - MLR Dr. Ashish Verma, IISc Bangalore

T RAVEL 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 observations were considered The obtained model had a poor r square value of Dr. Ashish Verma, IISc Bangalore

T RAVEL 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. 45 Category Of buses Weighted averages of speeds in kmph Big Volvo21.62 Normal15.82 Dr. Ashish Verma, IISc Bangalore

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

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

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

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

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

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

W ALK THROUGH 52 Dr. Ashish Verma, IISc Bangalore

C ONCLUSIONS Most of the approaches for transit PIS design - direct cost / the in-vehicle travel time - optimum path for making a trip. The GC based PIS design for trip itinerary planning is suitable for the Indian scenario. In the present work, a unique PIS design suitable for Indian conditions, for multimodal transit system is proposed. The same has been applied on commercially available GIS software TransCAD. It will answer user queries taking into account a multi-objective GC based approach. 53 Dr. Ashish Verma, IISc Bangalore

C ONCLUSIONS A system architecture is developed and proposed to obtain optimum path, on a multimodal transit network, based on GC. The developed model of transit PIS, is demonstrated on a real world transit network of Bangalore City, India. 54 Dr. Ashish Verma, IISc Bangalore

F UTURE S COPE The travel time model developed was statistically poor. A thorough research is required to analyze the travel pattern in Bangalore and a good model could be developed. If a continuous GPS data on arrival of buses is used to obtain the real time data which in turn is used in the travel time modeling then the accuracy of the proposed system will enhance 55 Dr. Ashish Verma, IISc Bangalore

56 Dr. Ashish Verma, IISc Bangalore Thank you Paper based on this work: Kasturia S. and Verma A., (2010). “Multiobjective Transit Passenger Information System Design Using GIS” Journal of Urban Planning and Development, ASCE, Vol. 136, No. 1