Optimal design of on-demand modular feeder transit services

Authors

• Xiaoling Luo
• Wenbo Fan
• Meng Xu
• Xiaotian Yan

Document Type

Journal Article

Publication Date

2025

Subject Area

economics - operating costs, infrastructure - fleet management, infrastructure - interchange/transfer, infrastructure - vehicle, mode - bus, mode - other, place - north america, place - urban, planning - route design, ridership - demand

Keywords

On-demand feeder transit, Modular autonomous vehicles, Optimal design, Continuum approximation, Decoupling operation

Abstract

Modular Autonomous Vehicles (MAVs) enable transit agencies to adapt to fluctuating demand by flexibly coupling them into platoons for dispatch. We propose decoupling these platoons into individual MAVs for on-demand feeder transit, which connects a transportation hub and offers door-to-door services to patrons across a distant region. This Modular Feeder Transit (MFT) reduces routing distance and time to visit multiple points within the service region, traditionally accomplished by a single bus vehicle but now by several MAVs, each covering a subset of the total points. However, careful consideration is needed for recoupling MAVs on return trips, as delays may arise from waiting for the last MAV. To assess MFT’s effectiveness, we propose an optimal design model to determine key operational features for MFT such as platoon sizes at dispatch, dispatch headways, zone partitions, and recoupling strategies, tailored to non-uniform demand distributions. Using continuum approximation, we derive analytical expressions for system metrics, including routing time, waiting time, line-haul travel time, and operational costs. Closed-form relationships for optimal conditions lead to an efficient solution algorithm. Numerical studies show that MFT consistently outperforms traditional fixed-capacity feeder bus transit, achieving over 10% cost savings in certain scenarios. Notably, the advantage of MFT diminishes with high routing time variance. This underscores the necessity of advanced operational algorithms to reduce MAV trip variance and leverage the flexibility of MAVs in practice.

Rights

Permission to publish the abstract has been given by Elsevier, copyright remains with them.

Comments

Transportation Research Part C Home Page:

http://www.sciencedirect.com/science/journal/0968090X

Recommended Citation

Luo, X., Fan, W., Xu, M., & Yan, X. (2025). Optimal design of on-demand modular feeder transit services. Transportation Research Part C: Emerging Technologies, 178, 105245.