Seamless Multimodal Transportation Scheduling

TL;DR

This paper introduces a novel optimization system for last-mile transportation that efficiently coordinates ride-hailing and mass transit, improving predictability and reducing environmental impact in urban mobility.

Contribution

It presents a new decision diagram-based branch-and-price model for large-scale multimodal scheduling, significantly faster than existing methods.

Findings

The proposed model solves real-world instances in about 1 minute.

Aggregating passengers by destination yields high-quality solutions.

The problem is NP-hard, but the method effectively handles large datasets.

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 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 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.