Development of Application for Estimating Daily Boarding and Alighting Counts on New York City Buses

Alex Lu · Brian S. Levine

8 citations0/0 files storedTransportation Research Record Journal of the Transportation Research Board

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Transportation Planning and Optimization Human Mobility and Location-Based Analysis Urban Transport Systems Analysis

Abstract

To support bus service scheduling and planning, New York City Transit (NYCT) put into production a ridership application to determine surface transit boarding and alighting locations for each of approximately 2.8 million daily passenger trips on 218 bus routes. The application combined data from an automated vehicle location (AVL) system, a multimodal entry-only nongeographic automated fare collection (AFC) system, and general transit feed specification schedule file streams. To accomplish this objective, NYCT developed a highly optimized network-generation tool to estimate bus link loads and boarding and alighting locations by creating a scaled-down custom network based on first-bus trajectory (from AVL boarding location data) and a few possible AFC- or AVL-inferred second-leg pickup stops. Solving for the shortest walking path on this subnetwork yielded connection and alighting points more efficiently than did solving for all 128 million potential origin–destination (O-D) pairs systemwide. The program executes in less than 3 h in an automated environment that can support the operations management need for next-day reporting and the monitoring of patterns and trends in ridership behavior. The program also allows the aggregation of multiple days of route-level program output for schedule-making purposes and thus provides a significantly more representative understanding of passenger loads than were obtained historically from a few labor-intensive onboard observations (ridechecks) collected over a multiple-year period. Results were validated and found to be consistent with manual ridechecks, limited O-D surveys, and other sources. The accuracy obtained was sufficient to achieve acceptance of AVL-AFC data in lieu of traditional onboard observations for NYCT schedule making.

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References

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2016Bus Network Microsimulation with General Transit Feed Specification and Tap-in-Only Smart Card Data 2018A Data-Driven Approach to Prioritizing Bus Schedule Revisions at New York City Transit

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