A Dynamic Vulnerability Map to Assess the Risk of Road Network Traffic Utilization

TL;DR

This paper introduces a dynamic vulnerability map for assessing evacuation risks in road networks, utilizing GIS and community detection algorithms to adapt to traffic conditions and improve decision-making.

Contribution

It presents a novel dynamic vulnerability mapping approach that incorporates real-time traffic data and population displacement for better evacuation planning.

Findings

The dynamic map effectively visualizes evacuation difficulty across sectors.

Community detection algorithms adapt the vulnerability assessment to traffic changes.

The approach enhances decision-making in emergency management.

Abstract

Le Havre agglomeration (CODAH) includes 16 establishments classified Seveso with high threshold. In the literature, we construct vulnerability maps to help decision makers assess the risk. Such approaches remain static and do take into account the population displacement in the estimation of the vulnerability. We propose a decision making tool based on a dynamic vulnerability map to evaluate the difficulty of evacuation in the different sectors of CODAH. We use a Geographic Information system (GIS) to visualize the map which evolves with the road traffic state through a detection of communities in large graphs algorithm.