Adaptive emergency evacuation routing: a graph-based approach

Emergency evacuation plays a vital role in disaster management operations. The existing solutions for planning and routing emergency evacuations rely on preplanning based on prior information and lack adaptability to unprecedented conditions. This study introduces a novel adaptive method for emergency evacuation routing that dynamically reshapes the road network by actively determining and adjusting the direction of road segments and assigning traffic flow to them in order to improve the maximum possible flow of traffic evacuated from a set of dangerous zones to a set of safe zones. The proposed adaptive method revolves around a novel dynamic graph-based algorithm that iteratively and recursively distributes the traffic flow across the road network using a Depth First Search (DFS) approach. When the algorithm assigns the traffic flow to the road segments, it also determines the direction of the road segments to dynamically improve the performance of the evacuation process. The proposed method was implemented in a real-world evacuation scenario in the road network of Hoboken, New Jersey. The outcomes indicated a significant improvement of more than 74% in the number of successfully evacuated vehicles when the proposed algorithm was used, compared to state-of-the-art methods for evacuation routing that are unable to adjust road directions. The outcomes of this study help decision-makers and first responders develop dynamic and adaptive emergency evacuation plans for successful disaster management operations.

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Publication Details

Journal
International Journal of Transportation Science and Technology
Published
2024-12-03
DOI
https://doi.org/10.1016/j.ijtst.2024.11.010
Primary Topic
Evacuation and Crowd Dynamics
Type
article
Field-Weighted Citation Impact
0.00

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article

Adaptive emergency evacuation routing: a graph-based approach

Hojat Behrooz, Mohammad Ilbeigi
International Journal of Transportation Science and Technology
Evacuation and Crowd Dynamics
article

Adaptive emergency evacuation routing: a graph-based approach

Hojat Behrooz, Mohammad Ilbeigi
article en

Abstract

Emergency evacuation plays a vital role in disaster management operations. The existing solutions for planning and routing emergency evacuations rely on preplanning based on prior information and lack adaptability to unprecedented conditions. This study introduces a novel adaptive method for emergency evacuation routing that dynamically reshapes the road network by actively determining and adjusting the direction of road segments and assigning traffic flow to them in order to improve the maximum possible flow of traffic evacuated from a set of dangerous zones to a set of safe zones. The proposed adaptive method revolves around a novel dynamic graph-based algorithm that iteratively and recursively distributes the traffic flow across the road network using a Depth First Search (DFS) approach. When the algorithm assigns the traffic flow to the road segments, it also determines the direction of the road segments to dynamically improve the performance of the evacuation process. The proposed method was implemented in a real-world evacuation scenario in the road network of Hoboken, New Jersey. The outcomes indicated a significant improvement of more than 74% in the number of successfully evacuated vehicles when the proposed algorithm was used, compared to state-of-the-art methods for evacuation routing that are unable to adjust road directions. The outcomes of this study help decision-makers and first responders develop dynamic and adaptive emergency evacuation plans for successful disaster management operations.

International Journal of Transportation Science and TechnologyVol. 20
Stevens Institute of Technology (US)
National Science Foundation
Climate action
Openalex Percentile: Top 71%
Evacuation and Crowd Dynamics
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Adaptive emergency evacuation routing: a graph-based approach — Hojat Behrooz, Mohammad Ilbeigi · International Journal of Transportation Science and Technology (2024) | TGRS Research Map | TGRS