Optimizing Crowd Evacuation In The Emergency Route Planning Problem

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Date
2015-05
Authors
Akmal Khalid, Mohd Nor
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Abstract
Disastrous situations, either natural (e.g. fires, floods, hurricane) or man-made (e.g. terrorist bombings, chemical spills, etc.), have claimed the lives of thousands, triggering the needs for emergency evacuation. Typically, optimizing an emergency evacuation plan involves both the effectiveness in crowd modelling and route selection, where an optimum evacuation plan is vital in the emergency route planning (ERP) problem. Various ERP approaches have been developed which are classified into mathematical, decision-support, heuristic, and meta-heuristic approaches. Exhaustive literature reviews have shown the significance of bridging the gap between modeling and routing, where an integrated and viable approach is needed. In this study, an integrated evacuation planning framework utilizing crowd evacuation model and an artificial immune system (AIS) algorithm, called iEvaP, was proposed. iEvaP was validated against Lu et al. (2003) and its parameters were calibrated for optimum performance. In addition, to capture the dynamism in crowd that mimics the real world situation, dynamic group cohesion was incorporated to the framework, called iEvaP+, refurbishing the integrated evacuation planning with dynamism. The approach was tested on the public data and the results showed that the evacuation plan charted an improvement of up to 62% compared with capacity constrained route planner (CCRP) approach proposed by Lu et al. (2003). Subsequently, iEvaP+ was also applied to two case studies to evaluate its effectiveness and scalability with the real world situation. The results indicated the evacuation plan had obtained statistically significant improvement (p-value ≤ 0.05091 in most of the results) compared to CCRP approach.
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Electronic Computer
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