A Swarm Approach to Public Transit Using On-demand Routing in a Slime-Mold-Inspired Framework

2026-06-04

AI Summary

This study addresses the challenges of high operational costs, inefficient scheduling, and poor reliability that plague conventional demand-responsive transit systems under high-demand conditions. To overcome these limitations, the authors propose a slime-mold-inspired distributed scheduling framework that integrates decentralized swarm intelligence mechanisms, dynamic transfer strategies, and a continuous collaborative bidding algorithm. The approach is rigorously evaluated through simulations on real-world road networks derived from OpenStreetMap. By moving beyond traditional centralized, manually designed timetables, the proposed method significantly enhances system performance: passenger fulfillment rates increase by 28%, 49%, and 101% in suburban, urban, and semi-rural scenarios, respectively, while average walking times are reduced by over 75%. These results demonstrate a marked improvement in both service efficiency and scalability for demand-responsive transit systems.

Abstract

Demand-responsive transit (DRT) is a flexible alternative to traditional, fixed-route mass-transit networks. Although DRT can function well in low-density communities, high operating costs and low reliability are common issues. We propose that these issues can be mitigated by moving from a centralized, manually-scheduled scheme to a distributed system capable of dynamically routing multiple vehicles using a slime-mold-inspired routing algorithm to maximize network effectiveness. We additionally introduce the method of dynamic transfers to further optimize transit network efficiency. All passenger allocation and dynamic transfers are handled via a continual cooperative bidding process by the buses. In this paper, we present simulated results for a swarm-driven transit network in suburban, urban, and semi-rural scenarios, using map networks pulled from OpenStreetMap. We show that our approach increases passenger delivery rates relative to a fixed-network approach by 28%, 49%, and 101%, respectively, and results in over 75% reduction in walking time in all cases.

Problem

• Demand-responsive transit
• operating cost
• reliability
• public transit efficiency
• on-demand routing

Innovation

• slime-mold-inspired routing
• demand-responsive transit
• swarm intelligence
• dynamic transfers
• cooperative bidding