Vehicle rescue path planning for urban traffic waterlogging disaster based on GAST model

Rescue transportation will be severely hindered under urban waterlogging disasters. To solve the problem of delayed rescue of disaster vehicles, this study is based on a hydrodynamic model and uses a 2D hydrodynamic model to numerically analyze urban waterlogging. At the same time, a 1D pipeline network is used to calculate pipeline hydrodynamics and urban hydrodynamics modeling is completed by coupling 2D hydrodynamics with a 1D pipeline network. Considering the characteristics of urban transportation, static and dynamic path planning techniques are introduced to complete vehicle rescue path planning. In the analysis of hydrodynamic models, the monitoring value of the research model at the B water accumulation location was 41.2 cm, which was closer to the actual value and had a lower relative error, outperforming similar models. In addition, in the analysis of multiple rainfall scenarios, the proportion of short-term waterlogging caused by high-intensity rainfall was relatively high. For example, in scenario 4, the proportion of waterlogging formation within half an hour was 8.8%, which was higher than that of low rainfall. In addition, in static path planning, the research technique took 821s in scenario 4 and had a shorter planning distance, which is superior to similar techniques. In dynamic programming, the research technique performed better overall with a driving time of 1,054s in scenario 5 and a shorter planning distance of 7,723m. The research technology has good application effects in urban waterlogging disaster rescue. This research will provide technical support for urban disaster analysis and rescue.

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Publication Details

Journal
PLoS ONE
Published
2025-10-03
DOI
https://doi.org/10.1371/journal.pone.0332989
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
0.00

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article

Vehicle rescue path planning for urban traffic waterlogging disaster based on GAST model

Xiaohu You, Jianan Chen, Wenjun Cai, Chunhui Zhang
PLoS ONE
Flood Risk Assessment and Management
article

Vehicle rescue path planning for urban traffic waterlogging disaster based on GAST model

Xiaohu You, Jianan Chen, Wenjun Cai, Chunhui Zhang
article en

Abstract

Rescue transportation will be severely hindered under urban waterlogging disasters. To solve the problem of delayed rescue of disaster vehicles, this study is based on a hydrodynamic model and uses a 2D hydrodynamic model to numerically analyze urban waterlogging. At the same time, a 1D pipeline network is used to calculate pipeline hydrodynamics and urban hydrodynamics modeling is completed by coupling 2D hydrodynamics with a 1D pipeline network. Considering the characteristics of urban transportation, static and dynamic path planning techniques are introduced to complete vehicle rescue path planning. In the analysis of hydrodynamic models, the monitoring value of the research model at the B water accumulation location was 41.2 cm, which was closer to the actual value and had a lower relative error, outperforming similar models. In addition, in the analysis of multiple rainfall scenarios, the proportion of short-term waterlogging caused by high-intensity rainfall was relatively high. For example, in scenario 4, the proportion of waterlogging formation within half an hour was 8.8%, which was higher than that of low rainfall. In addition, in static path planning, the research technique took 821s in scenario 4 and had a shorter planning distance, which is superior to similar techniques. In dynamic programming, the research technique performed better overall with a driving time of 1,054s in scenario 5 and a shorter planning distance of 7,723m. The research technology has good application effects in urban waterlogging disaster rescue. This research will provide technical support for urban disaster analysis and rescue.

PLoS ONEVol. 20(10)
Wuhan University of Technology (CN), Wuhan University of Science and Technology (CN)
National Office for Philosophy and Social Sciences
Openalex Percentile: Top 74%
Flood Risk Assessment and Management
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