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

Cai X, Wang C, Chen S, Lu J - PLoS ONE (2016)

Bottom Line: 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.Finally, to validate the proposed procedure, actual crash data from Freeway G15 were compared with the safety prediction based on the risk matrix.

View Article: PubMed Central - PubMed

Affiliation: Transportation Research Center, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China.

ABSTRACT
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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Related in: MedlinePlus

A general view of the segment of National Freeway G15 (Kilometer post: k1184+275~k1215+870).
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pone.0149442.g001: A general view of the segment of National Freeway G15 (Kilometer post: k1184+275~k1215+870).

Mentions: The segment of National Freeway G15 is located between Suzhou city and Nantong city in Jiangsu Province, with about 32 km in length and a six-lane in both directions. And a general view of the segment is shown in Fig 1. Since the segment opened to traffic for only a few years, it could not accumulate enough crash data to be used for risk assessment of driving on rainy days. And crash data derived from other similar freeways in China could not be obtained, given possibly political impacts. Thus, a questionnaire to drivers was designed for collecting drivers’ risk perception on rainy days. In addition, some crash records collected from the segment were used to validate the proposed procedure of risk assessment.


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

Cai X, Wang C, Chen S, Lu J - PLoS ONE (2016)

A general view of the segment of National Freeway G15 (Kilometer post: k1184+275~k1215+870).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4764618&req=5

pone.0149442.g001: A general view of the segment of National Freeway G15 (Kilometer post: k1184+275~k1215+870).
Mentions: The segment of National Freeway G15 is located between Suzhou city and Nantong city in Jiangsu Province, with about 32 km in length and a six-lane in both directions. And a general view of the segment is shown in Fig 1. Since the segment opened to traffic for only a few years, it could not accumulate enough crash data to be used for risk assessment of driving on rainy days. And crash data derived from other similar freeways in China could not be obtained, given possibly political impacts. Thus, a questionnaire to drivers was designed for collecting drivers’ risk perception on rainy days. In addition, some crash records collected from the segment were used to validate the proposed procedure of risk assessment.

Bottom Line: 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.Finally, to validate the proposed procedure, actual crash data from Freeway G15 were compared with the safety prediction based on the risk matrix.

View Article: PubMed Central - PubMed

Affiliation: Transportation Research Center, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus