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Study on Risk Prediction Model of Expressway Agglomerate Fog-Related Accidents

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Title: Study on Risk Prediction Model of Expressway Agglomerate Fog-Related Accidents
Authors: Jianyang Song, Hua Tian, Xiaoyu Yuan, Jingjing Gao, Xihui Yin, Zhi Wang, Meichao Qian, Hengtong Zhang
Source: Atmosphere, Vol 14, Iss 6, p 960 (2023)
Publisher Information: MDPI AG, 2023.
Publication Year: 2023
Collection: LCC:Meteorology. Climatology
Subject Terms: expressway, agglomerate fog, risk level prediction of fog-related accidents, meteorological conditions, road hidden dangers, traffic flow conditions, Meteorology. Climatology, QC851-999
More Details: Based on meteorological observations, traffic flow data and information of traffic accidents caused by fog or agglomerate fog along the expressways in Jiangsu Province and Anhui Province in China from 2012 to 2021, key impact factors including meteorological conditions, road hidden dangers and traffic flow conditions are integrated to establish the prediction model for risk levels of expressway agglomerate fog-related accidents. This model takes the discrimination of the occurrence conditions of agglomerate fog as the starting term, and determines the hazard levels of agglomerate fog-related accidents by introducing the probability prediction value of meteorological conditions for fog-related accident as the disaster-causing factor. On this basis, the hourly road traffic flow and the location of road sections with a hidden danger of agglomerate fog are taken as traffic and road factors to construct the correction scheme for the hazard levels, and the final predicted risk level of agglomerate fog-related accident is obtained. The results show that for the criteria of disaster-causing factor classification threshold, 72.3% of fog-related accidents correspond to a hazard of a medium level or above, and 86.2% of the road traffic flow conditions are consistent with the levels of the traffic factor defined based on parametric indexes. For risk level prediction, six out of the seven agglomerate fog-related accidents correspond to the level of higher risk or above, which can help provide meteorological support for traffic safety under severe weather conditions. Moreover, the model takes into account the impacts of traffic flow and the road environment, which is conducive to further improving the reliability of the risk assessment results.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2073-4433
Relation: https://www.mdpi.com/2073-4433/14/6/960; https://doaj.org/toc/2073-4433
DOI: 10.3390/atmos14060960
Access URL: https://doaj.org/article/ec8cbc463fa44ca59abfa4b182a0eaf8
Accession Number: edsdoj.8cbc463fa44ca59abfa4b182a0eaf8
Database: Directory of Open Access Journals
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  Data: Study on Risk Prediction Model of Expressway Agglomerate Fog-Related Accidents
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  Data: <searchLink fieldCode="AR" term="%22Jianyang+Song%22">Jianyang Song</searchLink><br /><searchLink fieldCode="AR" term="%22Hua+Tian%22">Hua Tian</searchLink><br /><searchLink fieldCode="AR" term="%22Xiaoyu+Yuan%22">Xiaoyu Yuan</searchLink><br /><searchLink fieldCode="AR" term="%22Jingjing+Gao%22">Jingjing Gao</searchLink><br /><searchLink fieldCode="AR" term="%22Xihui+Yin%22">Xihui Yin</searchLink><br /><searchLink fieldCode="AR" term="%22Zhi+Wang%22">Zhi Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Meichao+Qian%22">Meichao Qian</searchLink><br /><searchLink fieldCode="AR" term="%22Hengtong+Zhang%22">Hengtong Zhang</searchLink>
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  Data: Atmosphere, Vol 14, Iss 6, p 960 (2023)
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  Data: MDPI AG, 2023.
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  Data: 2023
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  Data: LCC:Meteorology. Climatology
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  Data: <searchLink fieldCode="DE" term="%22expressway%22">expressway</searchLink><br /><searchLink fieldCode="DE" term="%22agglomerate+fog%22">agglomerate fog</searchLink><br /><searchLink fieldCode="DE" term="%22risk+level+prediction+of+fog-related+accidents%22">risk level prediction of fog-related accidents</searchLink><br /><searchLink fieldCode="DE" term="%22meteorological+conditions%22">meteorological conditions</searchLink><br /><searchLink fieldCode="DE" term="%22road+hidden+dangers%22">road hidden dangers</searchLink><br /><searchLink fieldCode="DE" term="%22traffic+flow+conditions%22">traffic flow conditions</searchLink><br /><searchLink fieldCode="DE" term="%22Meteorology%2E+Climatology%22">Meteorology. Climatology</searchLink><br /><searchLink fieldCode="DE" term="%22QC851-999%22">QC851-999</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Based on meteorological observations, traffic flow data and information of traffic accidents caused by fog or agglomerate fog along the expressways in Jiangsu Province and Anhui Province in China from 2012 to 2021, key impact factors including meteorological conditions, road hidden dangers and traffic flow conditions are integrated to establish the prediction model for risk levels of expressway agglomerate fog-related accidents. This model takes the discrimination of the occurrence conditions of agglomerate fog as the starting term, and determines the hazard levels of agglomerate fog-related accidents by introducing the probability prediction value of meteorological conditions for fog-related accident as the disaster-causing factor. On this basis, the hourly road traffic flow and the location of road sections with a hidden danger of agglomerate fog are taken as traffic and road factors to construct the correction scheme for the hazard levels, and the final predicted risk level of agglomerate fog-related accident is obtained. The results show that for the criteria of disaster-causing factor classification threshold, 72.3% of fog-related accidents correspond to a hazard of a medium level or above, and 86.2% of the road traffic flow conditions are consistent with the levels of the traffic factor defined based on parametric indexes. For risk level prediction, six out of the seven agglomerate fog-related accidents correspond to the level of higher risk or above, which can help provide meteorological support for traffic safety under severe weather conditions. Moreover, the model takes into account the impacts of traffic flow and the road environment, which is conducive to further improving the reliability of the risk assessment results.
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  Data: https://www.mdpi.com/2073-4433/14/6/960; https://doaj.org/toc/2073-4433
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      Pagination:
        PageCount: 1
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      – SubjectFull: expressway
        Type: general
      – SubjectFull: agglomerate fog
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      – SubjectFull: risk level prediction of fog-related accidents
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      – SubjectFull: QC851-999
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            NameFull: Jianyang Song
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