Model Development for Risk Assessment of Driving on Freeway under Rainy Weather Conditions

Rainy weather conditions could result in significantly negative impacts on driving on freeways. However, due to lack of enough historical data and monitoring facilities, many regions are not able to establish reliable risk assessment models to identify such impacts. Given the situation, this paper p...

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Veröffentlicht in:PloS one 2016-02, Vol.11 (2), p.e0149442-e0149442
Hauptverfasser: Cai, Xiaonan, Wang, Chen, Chen, Shengdi, Lu, Jian
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description Rainy weather conditions could result in significantly negative impacts on driving on freeways. However, due to lack of enough historical data and monitoring facilities, many regions are not able to establish reliable risk assessment models to identify such impacts. Given the situation, this paper provides an alternative solution where the procedure of risk assessment is developed based on drivers' subjective questionnaire and its performance is validated by using actual crash data. First, an ordered logit model was developed, based on questionnaire data collected from Freeway G15 in China, to estimate the relationship between drivers' perceived risk and factors, including vehicle type, rain intensity, traffic volume, and location. Then, weighted driving risk for different conditions was obtained by the model, and further divided into four levels of early warning (specified by colors) using a rank order cluster analysis. After that, a risk matrix was established to determine which warning color should be disseminated to drivers, given a specific condition. Finally, to validate the proposed procedure, actual crash data from Freeway G15 were compared with the safety prediction based on the risk matrix. The results show that the risk matrix obtained in the study is able to predict driving risk consistent with actual safety implications, under rainy weather conditions.
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However, due to lack of enough historical data and monitoring facilities, many regions are not able to establish reliable risk assessment models to identify such impacts. Given the situation, this paper provides an alternative solution where the procedure of risk assessment is developed based on drivers' subjective questionnaire and its performance is validated by using actual crash data. First, an ordered logit model was developed, based on questionnaire data collected from Freeway G15 in China, to estimate the relationship between drivers' perceived risk and factors, including vehicle type, rain intensity, traffic volume, and location. Then, weighted driving risk for different conditions was obtained by the model, and further divided into four levels of early warning (specified by colors) using a rank order cluster analysis. After that, a risk matrix was established to determine which warning color should be disseminated to drivers, given a specific condition. Finally, to validate the proposed procedure, actual crash data from Freeway G15 were compared with the safety prediction based on the risk matrix. The results show that the risk matrix obtained in the study is able to predict driving risk consistent with actual safety implications, under rainy weather conditions.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>26894434</pmid><doi>10.1371/journal.pone.0149442</doi><oa>free_for_read</oa></addata></record>
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subjects Algorithms
Analysis
Automobile Driving
Cluster analysis
Drivers
Driving conditions
Earth Sciences
Engineering and Technology
Fatalities
Highways
Humans
Models, Theoretical
Motor vehicle driving
People and Places
Physical Sciences
Rain
Research and Analysis Methods
Risk analysis
Risk Assessment
Risk factors
Risk perception
Safety
Studies
Traffic accidents & safety
Traffic models
Traffic volume
Transportation planning
Weather
Weather conditions
title Model Development for Risk Assessment of Driving on Freeway under Rainy Weather Conditions
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