Multi-agent Optimization of Non-cooperative Multimodal Mobility Systems

TL;DR

This paper develops a mathematical framework to analyze how decentralized decision-makers in multimodal mobility systems influence overall efficiency, providing insights for policy design and system optimization.

Contribution

It introduces a unified, convexified modeling approach for equilibrium pricing in non-cooperative multimodal transportation systems, enabling efficient analysis of decentralized decision impacts.

Findings

Travelers prefer ride-sourcing and multimodal options when prices are low.

Subsidies may be needed for multimodal use in less connected networks.

More transit hubs increase ride-sourcing driver relocation time.

Abstract

While multimodal mobility systems have the potential to bring many benefits to travelers, drivers, the environment, and traffic congestion, such systems typically involve multiple non-cooperative decision-makers who may selfishly optimize their own objectives without considering the overall system benefits. This paper aims to investigate market-based interactions of travelers and ride-sourcing drivers in the context of multimodal mobility systems. We propose a unified mathematical modeling framework to capture the decentralized travelers and drivers' decision-making process and balance the network's demand and supply by equilibrium pricing. Such a model allows analyses of the impact of decentralized decision-making on multimodal mobility efficiencies. The proposed formulation can be further convexified to efficiently compute the equilibrium ride-sourcing prices. We conduct numerical experiments on different settings of transportation networks to gain policy insights. We find that travelers prefer ride-sourcing and multimodal transportation more than the driving option when they are more sensitive to prices. We also find that travelers may need to be subsidized to use multimodal transportation when there is fewer transit hubs in the network or, ride-sourcing drivers become too sensitive to the prices. However, we find that more transit hubs in the network increases the total empty VMT of ride-sourcing drivers by increasing the total relocation time. The proposed model can be used by policymakers and platform operators to design pricing and subsidy schemes that align individual decision-making with system-level efficiency and evaluate the trade-offs between accessibility and environmental impacts in multimodal transportation networks.