Emergency transportation network design problem: Identification and evaluation of disaster response routes

Nikoo, Nariman; Babaei, Mohsen; Mohaymany, Afshin Shariat

doi:10.1016/j.ijdrr.2017.07.003

Cited by 54 publications

(35 citation statements)

References 49 publications

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“…Therefore, the probability given stochastically does not reflect the disturbance mechanism in real world. For emergency path planning, performance metrics, especially the timeliness, are adopted by related literatures, like [2,17,27]. However, the time delay is given at random, which may lead to misconception for planner during making the decision.…”

Section: Literature Discussion and Contributions In Is Papermentioning

confidence: 99%

“…Based on the timeliness, Bahaabadi et al [25] present a model with multiobjective to find an optimal path from road network, in which the time-dependent stochastic travel time is taken into account. Inversely, Nikoo et al [2] propose a threeobjective interprogramming model to seek the path with best robustness by sacrificing the timeliness. Regarding the algorithm, genetic algorithm is frequently used in related researches, such as [26][27][28].…”

Section: Emergency Transportation Network (Etn)mentioning

confidence: 99%

“…Obviously, the problem of TNPES belongs to the category of decision-making under uncertainty and risk. Supported by the policies and regulations, the selected path will be specialized for transporting emergency materials [2]. Without the interruption of other traffic during the emergency period, the transportation of emergency materials can be guaranteed in a great degree.…”

Section: Introductionmentioning

confidence: 99%

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Discrete Robustness Optimization on Emergency Transportation Network Based on Prospect Theory

Zhang

Zhu

Wang

et al. 2019

Journal of Advanced Transportation

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This paper focuses on the discrete robustness optimization of emergency transportation network with the consideration of timeliness and decision behavior of decision-maker under the limited rationality. Based on a situation that the nearer to disaster area, the higher probability of time delay, prospect theory is specially introduced to reflect the subjective decision behavior of decision-maker. Then, a discrete robustness optimization model is proposed with the purpose of the better timeliness and robustness. The model is based on the emergency transportation network with multistorage centers and multidisaster points. In order to obtain the optimal solution, an improved genetic algorithm is designed by introducing a bidirectional search strategy based on a newfangled path cluster to obtain specific paths that connect each storage centers and each disaster points. Finally, a case study is exhibited to demonstrate the reasonability of the model, theory, and algorithm. The result shows that the path cluster with the better timeliness and robustness can be well obtained by using the prospect theory and improved genetic algorithm. The analysis especially reveals that the robustness is correspondent to the risk aversion in prospect theory.

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Section: Literature Discussion and Contributions In Is Papermentioning

confidence: 99%

Section: Emergency Transportation Network (Etn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discrete Robustness Optimization on Emergency Transportation Network Based on Prospect Theory

Zhang

Zhu

Wang

et al. 2019

Journal of Advanced Transportation

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“…Regarding the minimum time as the target, Nikoo et al. [9] has proposed a model for the emergency transportation network design problem. The model is solved by branch cutting method, taking the travel length, travel time and the number of paths as metrics to identify the optimal routes for emergency vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic generation of optimal road trajectory for the rescue vehicle in case of emergency on mountain freeway using reinforcement learning approach

Yang

Yuan

et al. 2021

IET Intelligent Trans Sys

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Rapid rescue response has the highest priority in case of emergency randomly happening on the freeway network, which allows rescue vehicles to have many trajectory options. Searching for the fastest way is not easy within a short time after traffic accident happens especially for the mountainous area with special characteristics such as limited traffic capacity, enclosed internal space and so on. Here, road segment model is proposed to determine smallest road segment covering possible rescue ways. Other than traditional optimal search methods, modified reinforcement-leaning is introduced to find the optimal road trajectory. The proposed methods are tested in the freeway of Qinling Tunnel group, Xihan Freeway of Shaanxi province, China as a case study. Compared with traditional shortest path method, the rescue vehicle arrival time to the accident location is shortened from 22.9 to 6.5 min and dissipation time is also shortened from 52.4 to 25.6 min. Both of them show the proposed road trajectory could improve the rescue effectiveness and reduce the influence to road network. Successful application of these case study shows they could probably extend to use to other scenarios and contribute to improve the intelligence transportation system.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The model considers five objective functions and a two-stage heuristic method is applied to solve the vehicle routing problem for large-scale instances. Nikoo et al [2018] presented a multi-objective optimization model for the transportation network design problem in emergencies, determining the optimal emergency response for trips with high priority in the aftermath of earthquakes.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved robust shortest paths by penalized investments

Pérez-Galarce

Candia-Véjar

Maculan³

et al. 2021

RAIRO-Oper. Res.

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Connectivity after disasters has become a critical problem in the management of modern cities. This comes from the need of the decision-makers to ensure urgent medical attention by providing access to hospitals and also to other relevant services needed by the population. Managing congestion could help maintain some routes operative even in complex scenarios such as natural disasters, terrorist attacks, protests or riots. Recent advances in humanitarian logistics have faced this problem using different modeling approaches, but by principally focusing on the response phase. In this paper, firstly, we propose a penalized variant of an existing mathematical model for the robust s-t path problem with investments. With the aim to solve the robust several-to-one path problem with investments, and due to the high complexity of this new problem, a heuristic to solve it is proposed. Moreover, this approach allows us to improve travel times in both specific paths and a set of routes in a systemic framework. The new problem and the proposed heuristic are illustrated by using an example, which corresponds to a typical city network, that provides a concrete vision about the potential application of the framework. Lastly, some managerial insights are given when the analysis of results is exhibited in the example network

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Emergency transportation network design problem: Identification and evaluation of disaster response routes

Cited by 54 publications

References 49 publications

Discrete Robustness Optimization on Emergency Transportation Network Based on Prospect Theory

Discrete Robustness Optimization on Emergency Transportation Network Based on Prospect Theory

Automatic generation of optimal road trajectory for the rescue vehicle in case of emergency on mountain freeway using reinforcement learning approach

Improved robust shortest paths by penalized investments

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