Seamless Multimodal Transportation Scheduling

Arvind U. Raghunathan (), David Bergman (), John N. Hooker (), Thiago Serra () and Shingo Kobori ()
Additional contact information
Arvind U. Raghunathan: Mitsubishi Electric Research Labs, Cambridge, Massachusetts 02139
David Bergman: Operations and Information Management, University of Connecticut, Storrs, Connecticut 06260
John N. Hooker: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Thiago Serra: Freeman College of Management, Bucknell University, Lewisburg, Pennsylvania 17837
Shingo Kobori: Advanced Technology R&D Center, Mitsubishi Electric Corporation, Hyogo 661-8661, Japan

INFORMS Journal on Computing, 2024, vol. 36, issue 2, 336-358

Abstract: Ride-hailing services have expanded the role of shared mobility in passenger transportation systems, creating new markets and creative planning solutions for major urban centers. In this paper, we consider their use for the first-mile or last-mile passenger transportation in coordination with a mass transit service to provide a seamless multimodal transportation experience for the user. A system that provides passengers with predictable information on travel and waiting times in their commutes is immensely valuable. We envision that the passengers will inform the system of their desired travel and arrival windows so that the system can jointly optimize the schedules of passengers. The problem we study balances minimizing travel time and the number of trips taken by the last-mile vehicles, so that long-term planning, maintenance, and environmental impact are all taken into account. We focus on the case where the last-mile service aggregates passengers by destination. We show that this problem is NP-hard, and we propose a decision diagram–based branch-and-price decomposition model that can solve instances of real-world size (10,000 passengers spread over an hour, 50 last-mile destinations, 600 last-mile vehicles) in computational time (∼1 minute) that is orders of magnitude faster than the solution times of other methods appearing in the literature. Our experiments also indicate that aggregating passengers by destination on the last-mile service provides high-quality solutions to more general settings.

Keywords: last mile; mass transit; scheduling; decision diagrams; branch and price (search for similar items in EconPapers)
Date: 2024
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