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Article | Open Access

Multiclass dynamic emergency traffic collaborative assignment with parallel two-stage optimization

School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
School of Economics and Management, Xi'an Technological University, Xi'an 710021, China
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Abstract

In the response of disaster relief, there are multiclass emergency traffic consisting of private evacuation vehicles and public rescue vehicles on the road network, and they have mutually interfering dynamic operation processes and respective emergency response goals. With the purpose of realizing a collaborative operation between multiclass dynamic emergency traffic, the problem of rescue traffic priority and dynamic evacuation and rescue traffic collaborative assignment is considered, and formulate this problem as a bi-objective mathematical programming model. The model structure characteristics are then analyzed and a parallel two-stage solving approach is proposed, and prove an optimal solution can be obtained. Also, a general framework for evacuation and rescue traffic collaborative assignment optimization result dataset production is given. Moreover, some novel optimization-based data-driven conclusions for dynamic evacuation and rescue traffic collaborative assignment are captured, e.g., compared with the traditional hierarchical solving approach, the parallel solving approach is more resistant to interference from rescue traffic during the evacuation, and can achieve better evacuation traffic optimization performance without delaying rescue traffic operation; there exists the mirror symmetry law to time and space allocation of the road network for the quick arrival of rescue vehicles in the disaster position during the evacuation.

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Emergency Management Science and Technology
Article number: e023

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Cite this article:
Liu Z, Liu J-L, Zhang L. Multiclass dynamic emergency traffic collaborative assignment with parallel two-stage optimization. Emergency Management Science and Technology, 2024, 4: e023. https://doi.org/10.48130/emst-0024-0021

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Received: 29 May 2024
Accepted: 30 September 2024
Published: 05 November 2024
© 2024 by the author(s).

This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.