Demand-adaptive Transit Design for Urban Transportation Hubs

Conference · Thu Jul 30 04:00:00 EDT 2020

OSTI ID:1860362

Qian, Xinwu ^[1]; Xue, Jiawei; Lei, Zengxiang; Suarez, Juan; Ukkusuri, Satish V.

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1. Purdue University

In this study, we proposed a novel three-stage framework for planning the optimal demand-adaptive transit (DAT) at urban transportation hubs. Given the potential trip demand and road traffic condition, the proposed framework sequentially generates the optimal set of candidate routes, combines the outgoing routes and incoming routes at the hub, and derives the optimal fleet size and corresponding route frequency under the fixed budget. In particular, we build the route generation algorithm which maximizes passenger demand coverage with travel time deviation constraint. And a heuristic algorithm is further developed which yields near-optimal operation routes for real-time demand. The fleet optimization problem is formulated to minimize the weighted cost of energy savings, operation cost and trip revenue. We conduct comprehensive numerical experiments for planning DAT with electric buses at JFK airport in NYC using NYC taxi and for-hire vehicle trip data and GoogleMap speed data. The results show the superior performance of the proposed route generation algorithm which is able to cover citywide passenger demand with only 61 DAT routes. The results also suggest that the proposed DAT planning framework may serve over 47% of existing taxi and FHV demand by operating 18 routes using the fleet of 62 electric buses.

• Research Organization: Purdue University
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: EE0008524
• OSTI ID: 1860362
• Report Number(s): DOE-PURDUE-0008524-3
• Country of Publication: United States
• Language: English

Related Subjects

demand adaptive
electric bus
fleet optimization
route generation
transit planning
transportation hub