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Using Transit ITS Data for Service Planning University of Arizona, Transit Research Unit Alireza Khani, Sanggu Lee, Hyunsoo Noh, Neema Nassir, Mark Hickman.

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Presentation on theme: "Using Transit ITS Data for Service Planning University of Arizona, Transit Research Unit Alireza Khani, Sanggu Lee, Hyunsoo Noh, Neema Nassir, Mark Hickman."— Presentation transcript:

1 Using Transit ITS Data for Service Planning University of Arizona, Transit Research Unit Alireza Khani, Sanggu Lee, Hyunsoo Noh, Neema Nassir, Mark Hickman ITS Arizona 18th Annual Conference, Sep 29, 2011

2 Outline 2 Introduction / Motivations Data Data Analysis Applications

3 Introduction / Motivation 3 How to transform the massive data into useful information and support decision-making From fare card data, how to identify boarding stops how to infer alighting stops (known boarding) how to estimate travel/alighting time how to analyze travel pattern/behavior From Googles transit data, how to utilize a transit network how to develop an intermodal network how to find the shortest/optimal path

4 4 Automated Data Collection System Data Automatic Vehicle Location (AVL) Automatic Passenger Counting (APC) Automatic Fare Collection (AFC) Bus location based on GPS Bus systems based on sensors in doors Contactless smart card with unique ID Information (location,..) of a given vehicle Passenger boarding/alighting counts for stops Entry information (time, route, …) at the individual level 4.2 M Records3.4 M Records2.2M Records Metro Transit (www.metrotransit.org) data Serving Minneapolis/St. Paul area, MN November 2008 (30 days) Entry-only-control operation Implemented independently

5 Googles Transit Data 5 General Transit Feed Specification (GTFS) Open to public and frequently updated by agencies Stops, routes, trips, stop_times (schedules) Metro Transit in 2008 Routes.txt: 220 Stops.txt: 14,601 Trips.txt: 25,417 Stop_times.txt: 1,471,150

6 6 Relational Schema STOP TIMES CALENDARTRIPSROUTESSTOPS TRAVELER ROUTE NO DATE / TIME USE TYPE VEHICLE ID LOCATION A FC ROUTE ID ARRIVAL TIME TRIP ID DEPARTURE TIME STOP ID SEQUENCE APC with LOCATION ROUTE NO VEHICLE ID STOP ID SEQUENCE SCHEDULE STOP LOCATION STOP ID STOP CITY DESCRIPTION ROUTE NO TRIP IDSERVICE ID GENERAL TRANSIT FEED SPECIFICATION (GOOGLE) SCHEDULE VEHICLE Presented at 90 th TRB Annual Meeting in Washington, DC on January 2011 Automated Data Collection System (ADCS)

7 Data Analysis 7 DURATION 1DURATION 2DURATION 3 TRANSACTION 1 DATE / TIME ROUTE NUMBER USE TYPE BUS ID LOCATION USER SPECIAL SERIAL NO Purchases FARE CARD CARD TYPE METRO PASS U-PASS C-PASS Requests STORED VALUE TRANSACTION 2 DATE / TIME ROUTE NUMBER USE TYPE BUS ID LOCATION TRANSACTION 3 DATE / TIME ROUTE NUMBER USE TYPE BUS ID LOCATION TRANSACTION 4 DATE / TIME ROUTE NUMBER USE TYPE BUS ID LOCATION Access Time Waiting Time Activity Time Transfer Time Egress Time In-Vehicle Time Presented at 90 th TRB Annual Meeting in Washington, DC on January 2011

8 Data Analysis – cont. 8 Between-day Hourly Variation in Transactions by Card Type Duration Distribution by Card Type Presented at 90 th TRB Annual Meeting in Washington, DC on January 2011

9 ApplicationConference Name Travel Pattern Analysis90 th TRB Annual Meeting Transit O-D Estimation90 th TRB Annual Meeting (TRR in press) Transit Path Choice Model13 th TRB National Planning Applications Transit Path Algorithms Trip-Based Shortest Path91 st TRB Annual Meeting (under review) Intermodal Optimal Path91 st TRB Annual Meeting (under review) Intermodal Tour Path91 st TRB Annual Meeting (under review) Link-Based Transit Hyperpath91 st TRB Annual Meeting (under review) Transit Assignment and Simulation13 th TRB National Planning Applications Integration of Land Use and Transportation Temporal and Spatial Linkage between Land Use and Transit Demand 1 st World Symposium for Transport and Land Use Research Stop Aggregation Model91 st TRB Annual Meeting (under review) 9 Applications

10 Transit O-D Estimation Using Fare Card 10 Objective: To find the origin, destination, and transfer stops of each passenger in Minneapolis/St. Paul transit system Bus Walk Transaction Bus Stop Home Shopping Work Transfer Presented at 90 th TRB Annual Meeting in Washington, DC on January 2011 Application 1

11 Transit O-D Estimation – cont. 11 This model Finds the boarding and alighting stop for each AFC transaction, as well as the possible trip taken by the passenger Infers the transfers made by passenger by looking at the boarding and alighting times, service headway, etc Verifies the results by comparing with APC sample data Results for a typical weekday 90,154 transactions in AFC data set 84,413 transaction after refining the data set 33,514 transactions with estimated OD (28,260 persons) 98% percent verified by APC sample data Presented at 90 th TRB Annual Meeting in Washington, DC on January 2011 Application 1

12 Transit O-D Estimation – cont. 12 AMMid-dayPM Origin Destination Presented at 90 th TRB Annual Meeting in Washington, DC on January 2011 Application 1

13 Transit Path Choice Model Using Fare Card 13 Objective: To calibrate a logit model for transit path choice using the results of the O-D estimation study (Minneapolis/St. Paul) Origin Destination Passenger 1 Origin Destination Passenger 2 Origin Destination Choice set Presented at 13 th TRB National Planning Applications Conference in Reno, NV on May 2011 Application 2

14 Transit Path Choice Model – cont. 14 AttributeDefinition In Vehicle TimeVTSum of the times spent on rides of all legs of the path Number of TransfersTRNumber of bus transfers for the path Waiting TimeWTSum of waiting times for all the transfers in the path Walking DistanceWDSum of walking distances for all the transfers in the path Express RouteEXIndicates whether path contains any express routes or not Downtown RouteDTIndicates whether path contains a leg in downtown or not Covers ExpressCEXIndicates whether the users pass covers the express fare or not Covers DowntownCDTIndicates whether the users pass covers the downtown fare or not Presented at 13 th TRB National Planning Applications Conference in Reno, NV on May 2011 Application 2

15 Transit Path Choice Model – cont. 15 Calibration Tools Easy Logit Modeler (ELM) (http://www.elm-works.com)http://www.elm-works.com Biogeme (http://biogeme.epfl.ch)http://biogeme.epfl.ch Results Time Period# of RecordsModelRho 2 t-statistics Rush-Hours TR Non-Rush Hours TR All-Day TR Presented at 13 th TRB National Planning Applications Conference in Reno, NV on May 2011 Application 2

16 Transit Path Algorithms Using GTFS Data 16 Objective: To develop efficient algorithm for transit and intermodal shortest/optimal path Trip-Based Shortest Path (TBSP) Shortest path algorithm utilizing hierarchical transit network Intermodal Optimal Path (IOP) Optimal path with combined modes (auto and transit) Intermodal Optimal Tour (IOT) Optimal path for a tour with multiple destinations with combined modes Link-Based Transit Hyperpath Optimal strategy for schedule-based transit systems

17 Trip-Based Shortest Path (TBSP) 17 The idea Using trips as the elements of the network (instead of links) Distinguish between transfer stops and non-transfer stops in the labeling algorithm Performance in computation Sacramento, CASan Francisco (MUNI) No. of Stops (transfer) 2880 (1028) 4424 (2865) Improvement in Label Setting 83%54% Improvement in Label Correcting 32%26% Application 3

18 Intermodal Optimal Path (IOP) 18 This model Using TBSP for the transit side Using a multi-source time-dependent shortest path for the auto side Modeling Park-and-Ride locations to make the connections Contributions Solve for the optimal intermodal path and transfer point between an origin and a destination for a preferred arrival time The complexity is the same as single-mode path (e.g. TBSP) Application 4 Destination Origin

19 Intermodal Optimal Tour (IOT) 19 Objective Capture the effect of back trip on park-and-ride choice Find optimal tour and optimal park-and-ride facilities for a sequence of activities Application 5 Origin D1D1 D2D2 P1P1 P2P2 D3D3 D 10 D 20 P 10 P 20 D 11 D 12 D 22 D 21 P 11 P 22 D 32 D 31 D 30

20 Link-Based Transit Hyperpath 20 Objective: To search an optimal strategy path on a transit schedule network using logit model Link-Based Time-Expanded Network A run (or trip) segment between two consecutive stops is considered a unique link Search Model Application 6 ABCD eses e1e1 e2e2 e7e7 e8e8 e 10 e 12 etet [25, 7] Route 1 Route 2 Route 3 Route 4 [7, 1][6, 28] [4, 11] [4, 15] [10, 13] [cost, departure time] e3e3 [8, 5] e4e4 [7, 10] e9e9 [5, 16] e6e6 [6, 19] e5e5 [6, 8] e 14 [10, 43] e 11 [4, 21] [10, 23] e 13

21 Transit Assignment and Simulation 21 Integration among transportation models Activity-Based Model (ABM) for demand Dynamic Traffic Assignment (DTA) model FAST-TrIPs Passenger assignment and simulation on GTFS Transit Assignment and Simulation FAST-TrIPs Passenger assignment and simulation on GTFS Transit Assignment and Simulation DynusT MALTA Vehicle assignment and simulation DTA DynusT MALTA Vehicle assignment and simulation DTA ABM DaySim OpenAMOS ABM DaySim OpenAMOS Presented at 13 th TRB National Planning Applications Conference in Reno, NV on May 2011 Application 7

22 Transit Assignment and Simulation – cont. 22 Contributions Capability to model travel behaviors according to Googles GTFS which allows to connect to DTA model, such as boarding and alighting As well as integrating between ABM and DTA, the schedule-based transit assignment and simulation supports the intermodal capability Funded by FHWA EAR program and SHRP2 C-10B Application 7

23 Thank you!! 23 University of Arizona, Transit Research Unit


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