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

Outline Introduction / Motivations Data Data Analysis Applications

Introduction / Motivation 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 Google’s transit data, how to utilize a transit network how to develop an intermodal network how to find the shortest/optimal path

Automated Data Collection System Data Metro Transit (www.metrotransit.org) data Serving Minneapolis/St. Paul area, MN November 2008 (30 days) Entry-only-control operation Implemented independently 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 Records 3.4 M Records 2.2M Records

Google’s Transit Data 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

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

Presented at 90th TRB Annual Meeting in Washington, DC on January 2011 Data Analysis DURATION 1 DURATION 2 DURATION 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 TRANSACTION 3 TRANSACTION 4 Access Time Waiting Time Activity Time Transfer Time Egress Time In-Vehicle Time Presented at 90th TRB Annual Meeting in Washington, DC on January 2011

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

Applications Application Conference Name Travel Pattern Analysis 90th TRB Annual Meeting Transit O-D Estimation 90th TRB Annual Meeting (TRR in press) Transit Path Choice Model 13th TRB National Planning Applications Transit Path Algorithms Trip-Based Shortest Path 91st TRB Annual Meeting (under review) Intermodal Optimal Path Intermodal Tour Path Link-Based Transit Hyperpath Transit Assignment and Simulation Integration of Land Use and Transportation Temporal and Spatial Linkage between Land Use and Transit Demand 1st World Symposium for Transport and Land Use Research Stop Aggregation Model

Presented at 90th TRB Annual Meeting in Washington, DC on January 2011 Application 1 Transit O-D Estimation Using Fare Card 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 90th TRB Annual Meeting in Washington, DC on January 2011

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

Presented at 90th TRB Annual Meeting in Washington, DC on January 2011 Application 1 Transit O-D Estimation – cont. AM Mid-day PM Origin Destination Presented at 90th TRB Annual Meeting in Washington, DC on January 2011

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

Transit Path Choice Model – cont. Application 2 Transit Path Choice Model – cont. Attribute Definition In Vehicle Time VT Sum of the times spent on rides of all legs of the path Number of Transfers TR Number of bus transfers for the path Waiting Time WT Sum of waiting times for all the transfers in the path Walking Distance WD Sum of walking distances for all the transfers in the path Express Route EX Indicates whether path contains any express routes or not Downtown Route DT Indicates whether path contains a leg in downtown or not Covers Express CEX Indicates whether the user’s pass covers the express fare or not Covers Downtown CDT Indicates whether the user’s pass covers the downtown fare or not Presented at 13th TRB National Planning Applications Conference in Reno, NV on May 2011

Transit Path Choice Model – cont. Application 2 Transit Path Choice Model – cont. Calibration Tools Easy Logit Modeler (ELM) (http://www.elm-works.com) Biogeme (http://biogeme.epfl.ch) Results Time Period # of Records Model Rho2 t-statistics Rush-Hours 1225 -1.076 TR 0.029 -6.41 Non-Rush Hours 695 -1.010 TR 0.038 -4.76 All-Day 1922 -1.055 TR 0.032 -7.96 Presented at 13th TRB National Planning Applications Conference in Reno, NV on May 2011

Transit Path Algorithms Using GTFS Data 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

Improvement in Label Setting Improvement in Label Correcting Application 3 Trip-Based Shortest Path (TBSP) 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, CA San Francisco (MUNI) No. of Stops (transfer) 2880 (1028) 4424 (2865) Improvement in Label Setting 83% 54% Improvement in Label Correcting 32% 26%

Intermodal Optimal Path (IOP) Application 4 Intermodal Optimal Path (IOP) 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) Destination Origin

Intermodal Optimal Tour (IOT) Application 5 Intermodal Optimal Tour (IOT) 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 Origin D1 D2 P1 P2 D3 D10 D20 P10 P20 D11 D12 D22 D21 P11 P22 D32 D31 D30

Link-Based Transit Hyperpath Application 6 Link-Based Transit Hyperpath 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 e1 [cost, departure time] [25, 7] e5 [6, 8] e6 [6, 19] e10 [4, 15] e2 es [7, 1] e7 [6, 28] et e11 A B C [4, 21] D e3 [8, 5] e8 [4, 11] e12 [10, 13] e9 e13 e4 [5, 16] [10, 23] [7, 10] e14 [10, 43] Route 1 Route 3 Route 2 Route 4

Transit Assignment and Simulation Application 7 Transit Assignment and Simulation 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 ABM DaySim OpenAMOS DynusT MALTA Vehicle assignment and simulation DTA Presented at 13th TRB National Planning Applications Conference in Reno, NV on May 2011

Transit Assignment and Simulation – cont. Application 7 Transit Assignment and Simulation – cont. Contributions Capability to model travel behaviors according to Google’s 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

Thank you!! University of Arizona, Transit Research Unit