Time-Expanded Network Model of Train-Level Subway Ridership Flows Using Actual Train Movement Data
Subway ridership estimates are important to transit operators for both internal applications (e.g., setting service frequencies, prioritizing station upgrades) and external reporting (e.g., to the National Transit Database). New York City Transit (NYCT) is developing a new model that will accomplish three primary objectives: (a) estimating subway ridership at a train level for the first time, (b) basing path choice on actual train movements rather than on schedules so that uneven loadings can be accurately captured, and (c) running fast enough to be used daily and being sufficiently automated to run with minimal human intervention. The model integrates entry data from fare cards with actual train movement data from a wide range of electronic systems and schedules. The model assigns riders to trains by using a Frank–Wolfe approach, including Dijkstra’s algorithm for shortest paths, with customizations designed for transit. These customizations improve speed, enable the algorithm to model delays better, and allow for multiple types of riders with different preferences for transfers and crowding. The size and the complexity of the NYCT system make for a challenging test case computationally. Approximately 6 million trips are made on a busy weekday, and these are assigned to a time-expanded network containing more than 3 million nodes and 7 million arcs. The model is automated and runs fast enough that it can be used daily. Validation against manual counts indicates strong results, with the R2 for max load point volumes for the morning peak hour equal to .91.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9780309369848
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Authors:
- Stasko, Timon
- Levine, Brian
- Reddy, Alla
- Publication Date: 2016
Language
- English
Media Info
- Media Type: Print
- Features: Figures; References; Tables;
- Pagination: pp 92–101
- Monograph Title: Public Transportation, Volume 2: Passenger Rail and Terminals
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Serial:
- Transportation Research Record: Journal of the Transportation Research Board
- Issue Number: 2540
- Publisher: Transportation Research Board
- ISSN: 0361-1981
Subject/Index Terms
- TRT Terms: Algorithms; Data collection; Ridership; Subways; Transit riders
- Identifier Terms: New York City Transit Authority
- Geographic Terms: New York (New York)
- Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation;
Filing Info
- Accession Number: 01590286
- Record Type: Publication
- ISBN: 9780309369848
- Report/Paper Numbers: 16-4090
- Files: PRP, TRIS, TRB, ATRI
- Created Date: Feb 16 2016 2:36PM