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

Qifeng Zeng,A. Venugopal Reddy,Alex Lu,Brian Levine-2015-01-01-Transportation Research Record Journal of the Transportation Research Board
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TL;DRAbstract

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 syste

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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 syste

Keywords

Transport engineeringScheduleTransit (satellite)Automatic vehicle locationSubnetworkComputer scienceScheduling (production processes)Ticket

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