Location method for emergency rescue node on expressways based on spatio-temporal characteristics of vehicle operation

Expressway networks are continuously developing and emergency rescue demand is increasing proportionately. The location of expressway emergency rescue nodes needs refinement to meet changing requirements. In this study, the expressway was modeled as an expressway network. The differences in the origin destination (OD) distribution matrices for working days and major holidays were used as the bases for determining the need for temporary emergency rescue nodes. Overlapping and non-overlapping community detection algorithms were used to extract the distribution characteristics of OD during both day categories. These distributions were used to determine permanent and temporary emergency rescue sites. In this study, we considered the differences in traffic volume, distance, and impact of four vehicle types on traffic accidents to select the location of emergency rescue nodes, and allocate emergency resources. An emergency rescue node selection model for an expressway network was established based on spatio-temporal characteristics. The results based on a regional example determined that 22 permanent and 25 temporary emergency rescue nodes were appropriate. The average rescue time for traffic accidents during working days and major holidays compared to the P-center location model, was reduced by approximately 27.08% and 6.70%, respectively. The coefficient of variation of emergency rescue time was reduced by approximately 28.22% and 21.41%, respectively. The results indicated that the model satisfied the expressway emergency rescue demand requirements, and improved the rationality of the rescue center node layout.

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Publication Details

Journal
Scientific Reports
Published
2024-08-21
DOI
https://doi.org/10.1038/s41598-024-70532-1
Citations
3
Primary Topic
Evacuation and Crowd Dynamics
Type
article
Field-Weighted Citation Impact
1.01

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article

Location method for emergency rescue node on expressways based on spatio-temporal characteristics of vehicle operation

Xinghua Hu, Bing Long, Zhouzuo Wang, Wei Liu et al.
3 citations
Scientific Reports
Evacuation and Crowd Dynamics
1.01
article

Location method for emergency rescue node on expressways based on spatio-temporal characteristics of vehicle operation

Xinghua Hu, Bing Long, Zhouzuo Wang, Wei Liu, Jiahao Zhao, Hao Lei, Ran Wang
article en
3 citations

Abstract

Expressway networks are continuously developing and emergency rescue demand is increasing proportionately. The location of expressway emergency rescue nodes needs refinement to meet changing requirements. In this study, the expressway was modeled as an expressway network. The differences in the origin destination (OD) distribution matrices for working days and major holidays were used as the bases for determining the need for temporary emergency rescue nodes. Overlapping and non-overlapping community detection algorithms were used to extract the distribution characteristics of OD during both day categories. These distributions were used to determine permanent and temporary emergency rescue sites. In this study, we considered the differences in traffic volume, distance, and impact of four vehicle types on traffic accidents to select the location of emergency rescue nodes, and allocate emergency resources. An emergency rescue node selection model for an expressway network was established based on spatio-temporal characteristics. The results based on a regional example determined that 22 permanent and 25 temporary emergency rescue nodes were appropriate. The average rescue time for traffic accidents during working days and major holidays compared to the P-center location model, was reduced by approximately 27.08% and 6.70%, respectively. The coefficient of variation of emergency rescue time was reduced by approximately 28.22% and 21.41%, respectively. The results indicated that the model satisfied the expressway emergency rescue demand requirements, and improved the rationality of the rescue center node layout.

Scientific ReportsVol. 14(1)
Jilian Technology Group (China) (CN), Chongqing Municipal Health Commission (CN), Chongqing Jiaotong University (CN)
Chongqing Jiaotong University
Openalex Percentile: Top 25%
Evacuation and Crowd Dynamics
1.01
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