Online multi-modal evacuation during passenger flow outburst in urban transit system: A heterogeneous multi-agent reinforcement learning framework
Document Type
Journal Article
Publication Date
2025
Subject Area
place - urban, mode - bus, mode - subway/metro, mode - taxi, ridership - demand, technology - intelligent transport systems, operations - coordination, planning - methods
Keywords
Urban transit, Multi-modal evacuation, Online, Resilience, Multi-agent reinforcement learning
Abstract
With growing demand straining urban transit systems’ resilience in managing outburst passenger flows, existing approaches focused on offline and single-modal evacuations remain limited. This study proposes an online multi-modal evacuation framework that coordinates on-duty taxis, buses, and metros while minimizing impact on their regular services. We develop a data-driven agent-based environment to update multi-modal transit data and stranded passenger information in real time. Two coordination strategies are introduced: (1) an independent strategy using a decentralized training and distributed execution algorithm, and (2) a collaborative strategy using a hybrid centralized training and distributed execution algorithm. To dynamically assess evacuation effectiveness, we design a resilience framework with three metrics: robustness, rapidity, and resourcefulness. These metrics are transformed into demand-responsive feedback at each time step, enabling agents to proactively generate resilient evacuation plans. In a real-world case study triggered by a railway disruption, our approach outperforms genetic algorithms and multi-agent deep deterministic policy gradient algorithms in computation time and solution quality under offline conditions. Simulated new environments further validate its online applicability, demonstrating its potential for real-world deployment.
Rights
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Recommended Citation
Liu, E., Zhan, S., Zhu, Y., Lin, Z., & Wang, D. (2025). Online multi-modal evacuation during passenger flow outburst in urban transit system: A heterogeneous multi-agent reinforcement learning framework. Transportation Research Part E: Logistics and Transportation Review, 204, 104411.
Comments
Transportation Research Part E Home Page:
http://www.sciencedirect.com/science/journal/13665545