Incorporating dynamism in emergency route planning problem using immune-based approach

Abstract

The occurrence of emergency events tends to spark a very chaotic reaction, inducing a large surge of demand (number of evacuees) which exceeds the available resources (pathway/network). Planning a suitable evacuation route and identifying the shortest evacuation route before the occurrence of extreme events are crucial for an effective evacuation process. Although evacuation plans can be orchestrated in advance, probable crowd dynamics, especially group-based characteristics (e.g. group formation, group relation, group competition, etc.), may occur, rendering unfeasible evacuation plan. Therefore, creating a dynamic and effective emergency route planning (ERP) approach is important for crowd survivability. There are number of ERP approaches have been proposed, which includes mathematical-based approach, heuristic-driven approach, and meta-heuristic approach. Although the meta-heuristic approaches are capable of handling dynamic constraints and produces optimum result, its lack of adoption in the domain of ERP problem serve as an encouragement of applying this approach. Therefore, an integrated evacuation planning approach with dynamism (iEvaP+) is proposed to cater the dynamic of groups in crowd and optimizing the route selection, where the use of immune-based meta-heuristic approach had effectively produces statistically significant results (p-value < 0.5 significance level) against an approach proposed by [1].

Interactive demo

A looping preview of the emergency route-planning model: a navigation field routes shoppers to the exits while congestion and panic reshape the flow.