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SSRN Electronic Journal
Article . 2022 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2021
License: CC BY
Data sources: Datacite
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Ridesharing Evacuation Models of Disaster Response

Authors: Meng, Lingyu; Dong, Zhijie Sasha;

Ridesharing Evacuation Models of Disaster Response

Abstract

Timely evacuation is crucial to disaster response, as people can avoid suffering and loss of lives when a major disaster happens. With the development of sharing economy, ridesharing has the advantage of reducing congestion, saving travel time, and optimizing transportation mode to improve disaster evacuation efficiency. The paper proposes to integrate the concept of ridesharing into evacuation and develops a mixed-integer programming model for this problem. A real-world case study based on Houston is used to validate the proposed model. A series of instances are designed to compare the evacuation efficiency using two indicators, evacuation percentage and average travel distance. Results reveal that increasing the number of vehicles to help carless individuals might not be the most efficient method in this model. Moreover, this model offers a specific response strategy based on different disaster scales, which not only develops a better evacuation plan for the people but also provides relief agencies insights on resource utilization.

6 pages, 3 figures. Proceeding of the 2020 IISE Annual Conference, https://www.proquest.com/openview/403992

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Keywords

FOS: Computer and information sciences, Optimization and Control (math.OC), FOS: Mathematics, Applications (stat.AP), Mathematics - Optimization and Control, Statistics - Applications

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green