Simulation of Emergency Evacuation in Large Scale Metropolitan Railway Systems for Urban Resilience

TL;DR

This paper develops a large-scale railway evacuation simulation model to improve urban resilience, incorporating complex network features, optimization techniques, and real-world data, validated on Tokyo's extensive railway system.

Contribution

It introduces a novel simulation framework with optimization and subgraph partitioning for large-scale railway evacuation planning, addressing computational challenges and real-world applicability.

Findings

Optimized evacuation passenger flow and travel time during disruptions.

Validated model effectiveness on Tokyo's railway network.

Demonstrated capacity constraints are maintained during simulated evacuations.

Abstract

This paper presents a simulation for traffic evacuation during railway disruptions to enhance urban resilience. The research focuses on large-scale railway networks and provides flexible simulation settings to accommodate multiple node or line failures. The evacuation optimization model is mathematically formulated using matrix computation and nonlinear programming. The simulation integrates railway lines operated by various companies, along with external geographical features of the network. Furthermore, to address computational complexity in large-scale graph networks, a subgraph partitioning solution is employed for computation acceleration. The model is evaluated using the extensive railway network of Greater Tokyo. Data collection included both railway network structure and real-world GPS footfall data to estimate the number of station-area visitors for simulation input and evaluation purposes. Several evacuation scenarios were simulated for major stations including Tokyo, Shinjuku, Shibuya and so on. The results demonstrate that both evacuation passenger flow (EPF) and average travel time (ATT) during emergencies were successfully optimized, while remaining within the capacity constraints of neighboring stations and the targeted disruption recovery times.