Traffic emergency vehicle deployment and dispatch under uncertainty (2024)

Zhen, L., Wu, J., Chen, F. (2024). Traffic emergency vehicle deployment and dispatch under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 183, Article 103449. https://doi.org/10.1016/j.tre.2024.103449

Zhen, Lu ; Wu, Jingwen ; Chen, Fengli et al. / Traffic emergency vehicle deployment and dispatch under uncertainty. In: Transportation Research Part E: Logistics and Transportation Review. 2024 ; Vol. 183.

@article{aedadfb1af454389991b63a30e22ba6a,

title = "Traffic emergency vehicle deployment and dispatch under uncertainty",

abstract = "To manage traffic emergencies, cities require multiple types of traffic rescue vehicles, which need to be dispatched from various rescue stations dispersed throughout the city. A reasonable way of deploying the rescue vehicles must be determined given that the times and locations at which the traffic emergencies occur are uncertain. In this paper, we propose a two-stage stochastic programming approach to deploying multiple types of emergency vehicles in response to traffic accidents in the context of uncertainty. The first stage of the proposed model concerns decisions on the quantities of the different types of vehicles to be stocked at each rescue station. In the second stage, when the locations and accident rescue demands are realized in each scenario, the decision-making involves dispatch of the emergency vehicles to the traffic accidents. To solve the proposed model, we suggest a variable neighborhood search method. Using the road network of Jiading District, Shanghai, as an example, we perform numerical experiments to investigate the efficiency of the proposed method and the model validity. Some managerial implications are also outlined in the sensitivity analysis.",

keywords = "Emergency vehicle dispatching, Stochastic programming, Traffic accident rescue, Variable neighborhood search",

author = "Lu Zhen and Jingwen Wu and Fengli Chen and Shuaian Wang",

note = "Publisher Copyright: {\textcopyright} 2024 Elsevier Ltd",

year = "2024",

month = mar,

doi = "10.1016/j.tre.2024.103449",

language = "English",

volume = "183",

journal = "Transportation Research Part E: Logistics and Transportation Review",

issn = "1366-5545",

publisher = "Elsevier Ltd",

}

Zhen, L, Wu, J, Chen, F 2024, 'Traffic emergency vehicle deployment and dispatch under uncertainty', Transportation Research Part E: Logistics and Transportation Review, vol. 183, 103449. https://doi.org/10.1016/j.tre.2024.103449

Traffic emergency vehicle deployment and dispatch under uncertainty. / Zhen, Lu; Wu, Jingwen; Chen, Fengli et al.
In: Transportation Research Part E: Logistics and Transportation Review, Vol. 183, 103449, 03.2024.

Research output: Journal article publication › Journal article › Academic research › peer-review

TY - JOUR

T1 - Traffic emergency vehicle deployment and dispatch under uncertainty

AU - Zhen, Lu

AU - Wu, Jingwen

AU - Chen, Fengli

AU - Wang, Shuaian

N1 - Publisher Copyright:© 2024 Elsevier Ltd

PY - 2024/3

Y1 - 2024/3

N2 - To manage traffic emergencies, cities require multiple types of traffic rescue vehicles, which need to be dispatched from various rescue stations dispersed throughout the city. A reasonable way of deploying the rescue vehicles must be determined given that the times and locations at which the traffic emergencies occur are uncertain. In this paper, we propose a two-stage stochastic programming approach to deploying multiple types of emergency vehicles in response to traffic accidents in the context of uncertainty. The first stage of the proposed model concerns decisions on the quantities of the different types of vehicles to be stocked at each rescue station. In the second stage, when the locations and accident rescue demands are realized in each scenario, the decision-making involves dispatch of the emergency vehicles to the traffic accidents. To solve the proposed model, we suggest a variable neighborhood search method. Using the road network of Jiading District, Shanghai, as an example, we perform numerical experiments to investigate the efficiency of the proposed method and the model validity. Some managerial implications are also outlined in the sensitivity analysis.

AB - To manage traffic emergencies, cities require multiple types of traffic rescue vehicles, which need to be dispatched from various rescue stations dispersed throughout the city. A reasonable way of deploying the rescue vehicles must be determined given that the times and locations at which the traffic emergencies occur are uncertain. In this paper, we propose a two-stage stochastic programming approach to deploying multiple types of emergency vehicles in response to traffic accidents in the context of uncertainty. The first stage of the proposed model concerns decisions on the quantities of the different types of vehicles to be stocked at each rescue station. In the second stage, when the locations and accident rescue demands are realized in each scenario, the decision-making involves dispatch of the emergency vehicles to the traffic accidents. To solve the proposed model, we suggest a variable neighborhood search method. Using the road network of Jiading District, Shanghai, as an example, we perform numerical experiments to investigate the efficiency of the proposed method and the model validity. Some managerial implications are also outlined in the sensitivity analysis.

KW - Emergency vehicle dispatching

KW - Stochastic programming

KW - Traffic accident rescue

KW - Variable neighborhood search

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U2 - 10.1016/j.tre.2024.103449

DO - 10.1016/j.tre.2024.103449

M3 - Journal article

AN - SCOPUS:85184840724

SN - 1366-5545

VL - 183

JO - Transportation Research Part E: Logistics and Transportation Review

JF - Transportation Research Part E: Logistics and Transportation Review

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Zhen L, Wu J, Chen F, Wang S. Traffic emergency vehicle deployment and dispatch under uncertainty. Transportation Research Part E: Logistics and Transportation Review. 2024 Mar;183:103449. doi: 10.1016/j.tre.2024.103449