A Traffic Evacuation Model for Enhancing Resilience During Railway Disruption

TL;DR

The paper tackles resilience of railway systems under disruptions that affect multiple nodes by formulating a nonlinear programming evacuation model. It introduces a geography-featured, fused travel-cost matrix and a capacity-aware evacuation decision matrix $K$, aiming to minimize the average evacuation cost $obj(K) = \frac{J^{\top} (A_{cost} \odot K) J}{J^{\top} K J + \varepsilon}$ under a set of flow and capacity constraints. The approach integrates multi-operator infrastructure and external geography, enabling flexible simulations across many stations and lines. The model is validated on the Greater Tokyo network, demonstrating evacuation times below disruption duration and providing actionable insights for real-time resilience planning. The work contributes a scalable, interpretable framework for evacuation planning in large, heterogeneous rail networks with practical implications for emergency response and system recovery.

Abstract

This paper introduces a traffic evacuation model for railway disruptions to improve resilience. The research focuses on the problem of failure of several nodes or lines on the railway network topology. We proposed a holistic approach that integrates lines of various operator companies as well as external geographical features of the railway system. The optimized evacuation model was mathematically derived based on matrix computation using nonlinear programming. The model also takes into account the capacity of the surrounding evacuation stations, as well as the travel cost. Moreover, our model can flexibly simulate disruptions at multiple stations or any number of stations and lines, enhancing its applicability. We collected the large-scale railway network of the Greater Tokyo area for experimentation and evaluation. We simulated evacuation plans for several major stations, including Tokyo, Shinjuku, and Shibuya. The results indicate that the evacuation passenger flow (EPF) and the average travel time (ATT) during emergencies were optimized, staying within both the capacity limits of the targeted neighboring stations and the disruption recovery time.

Figures (5)

• Figure 1: The railway network of Greater Tokyo area
• Figure 2: The statistics of the station count of the 90 railway lines
• Figure 3: Three stations were highlighted as $\star$; The map is based on the transportation view of OpenStreetMap, where red dots show stations, and the black lines indicate railway lines.
• Figure 4: Box plots of station-level re-assigned passenger count (EPF) and travel time (PTT).
• Figure 5: Capacity and EPF of top 10 stations from the origins of Tokyo station, Shinjuku station and Shibuya station), along with the corresponding travel cost.