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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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0149442</identifier><identifier>PMID: 26894434</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>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</subject><ispartof>PloS one, 2016-02, Vol.11 (2), p.e0149442-e0149442</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Cai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2016 Cai et al 2016 Cai et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-8ffbbe82c03939b0b81272fbeb0e9704e6d572af95a952e1363e7ba1ea2d251c3</citedby><cites>FETCH-LOGICAL-c692t-8ffbbe82c03939b0b81272fbeb0e9704e6d572af95a952e1363e7ba1ea2d251c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4764618/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4764618/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26894434$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Hu, Xiaosong</contributor><creatorcontrib>Cai, Xiaonan</creatorcontrib><creatorcontrib>Wang, Chen</creatorcontrib><creatorcontrib>Chen, Shengdi</creatorcontrib><creatorcontrib>Lu, Jian</creatorcontrib><title>Model Development for Risk Assessment of Driving on Freeway under Rainy Weather Conditions</title><title>PloS one</title><addtitle>PLoS One</addtitle><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.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Automobile Driving</subject><subject>Cluster analysis</subject><subject>Drivers</subject><subject>Driving conditions</subject><subject>Earth Sciences</subject><subject>Engineering and Technology</subject><subject>Fatalities</subject><subject>Highways</subject><subject>Humans</subject><subject>Models, Theoretical</subject><subject>Motor vehicle driving</subject><subject>People and Places</subject><subject>Physical Sciences</subject><subject>Rain</subject><subject>Research and Analysis Methods</subject><subject>Risk analysis</subject><subject>Risk Assessment</subject><subject>Risk factors</subject><subject>Risk perception</subject><subject>Safety</subject><subject>Studies</subject><subject>Traffic accidents & safety</subject><subject>Traffic models</subject><subject>Traffic volume</subject><subject>Transportation planning</subject><subject>Weather</subject><subject>Weather 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Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cai, Xiaonan</au><au>Wang, Chen</au><au>Chen, Shengdi</au><au>Lu, Jian</au><au>Hu, Xiaosong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Model Development for Risk Assessment of Driving on Freeway under Rainy Weather Conditions</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2016-02-19</date><risdate>2016</risdate><volume>11</volume><issue>2</issue><spage>e0149442</spage><epage>e0149442</epage><pages>e0149442-e0149442</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><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.</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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