Integrating On-demand Ride-sharing with Mass Transit at-Scale

TL;DR

This paper proposes a hybrid urban mobility system combining on-demand ride-sharing with mass transit, using an optimized algorithmic framework to improve efficiency and sustainability based on real-world data from five US cities.

Contribution

It introduces a novel integer-linear programming model for multi-modal trip assignment integrating ride-sharing and mass transit, validated through extensive city-scale experiments.

Findings

Hybrid system outperforms pure on-demand models in efficiency.

Mass transit integration reduces congestion and environmental impact.

Algorithmic framework effectively optimizes multi-modal transportation operations.

Abstract

We are in the midst of a technology-driven transformation of the urban mobility landscape. However, unfortunately these new innovations are still dominated by car-centric personal mobility, which leads to concerns such as environmental sustainability, congestion, and equity. On the other hand, mass transit provides a means to move large amounts of travelers very efficiently, but is not very versatile and depends on an adequate concentration of demand. In this context, our overarching goal is to explore opportunities for new technologies such as ride-sharing to integrate with mass transit and provide a better service. More specifically, we envision a hybrid system that uses on-demand shuttles in conjunction with mass transit to move passengers efficiently, and provide an algorithmic framework for operational optimization. Our approach extends a state-of-the-art trip-vehicle assignment model to the multi-modal setting, where we develop a new integer-linear programming formulation to solve the problem efficiently. A comprehensive study covering five major cities in the United States based on real-world data is carried out to verify the advantages of such a system and the effectiveness of our algorithms. We show that our hybrid system provides significant improvements in comparison to a purely on-demand model by exploiting the efficiencies of the mass transit system.