Crash sequence based risk matrix for motorcycle crashes
•This study constructs a crash sequence based risk matrix to identify most frequent and most severe motorcycle crashes.•The information on crash sequences significantly improves the model goodness-of-fit in the crash severity models.•Five most frequent and most severe motorcycle crashes in the US we...
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| Veröffentlicht in: | Accident analysis and prevention 2018-08, Vol.117, p.21-31 |
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| creator | Wu, Kun-Feng Sasidharan, Lekshmi Thor, Craig P. Chen, Sheng-Yin |
| description | •This study constructs a crash sequence based risk matrix to identify most frequent and most severe motorcycle crashes.•The information on crash sequences significantly improves the model goodness-of-fit in the crash severity models.•Five most frequent and most severe motorcycle crashes in the US were identified.
Considerable research has been conducted related to motorcycle and other powered-two-wheeler (PTW) crashes; however, it always has been controversial among practitioners concerning with types of crashes should be first targeted and how to prioritize resources for the implementation of mitigating actions. Therefore, there is a need to identify types of motorcycle crashes that constitute the greatest safety risk to riders - most frequent and most severe crashes. This pilot study seeks exhibit the efficacy of a new approach for prioritizing PTW crash causation sequences as they relate to injury severity to better inform the application of mitigating countermeasures. To accomplish this, the present study constructed a crash sequence-based risk matrix to identify most frequent and most severe motorcycle crashes in an attempt to better connect causes and countermeasures of PTW crashes. Although the frequency of each crash sequence can be computed from crash data, a crash severity model is needed to compare the levels of crash severity among different crash sequences, while controlling for other factors that also have effects on crash severity such drivers’ age, use of helmet, etc. The construction of risk matrix based on crash sequences involve two tasks: formulation of crash sequence and the estimation of a mixed-effects (ME) model to adjust the levels of severities for each crash sequence to account for other crash contributing factors that would have an effect on the maximum level of crash severity in a crash. Three data elements from the National Automotive Sampling System - General Estimating System (NASS-GES) data were utilized to form a crash sequence: critical event, crash types, and sequence of events. A mixed-effects model was constructed to model the severity levels for each crash sequence while accounting for the effects of those crash contributing factors on crash severity. A total of 8039 crashes involving 8208 motorcycles occurred during 2011 and 2013 were included in this study, weighted to represent 338,655 motorcyclists involved in traffic crashes in three years (2011–2013)(NHTSA, 2013). The top five most frequent and severe types of c |
| doi_str_mv | 10.1016/j.aap.2018.03.022 |
| format | Article |
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Considerable research has been conducted related to motorcycle and other powered-two-wheeler (PTW) crashes; however, it always has been controversial among practitioners concerning with types of crashes should be first targeted and how to prioritize resources for the implementation of mitigating actions. Therefore, there is a need to identify types of motorcycle crashes that constitute the greatest safety risk to riders - most frequent and most severe crashes. This pilot study seeks exhibit the efficacy of a new approach for prioritizing PTW crash causation sequences as they relate to injury severity to better inform the application of mitigating countermeasures. To accomplish this, the present study constructed a crash sequence-based risk matrix to identify most frequent and most severe motorcycle crashes in an attempt to better connect causes and countermeasures of PTW crashes. Although the frequency of each crash sequence can be computed from crash data, a crash severity model is needed to compare the levels of crash severity among different crash sequences, while controlling for other factors that also have effects on crash severity such drivers’ age, use of helmet, etc. The construction of risk matrix based on crash sequences involve two tasks: formulation of crash sequence and the estimation of a mixed-effects (ME) model to adjust the levels of severities for each crash sequence to account for other crash contributing factors that would have an effect on the maximum level of crash severity in a crash. Three data elements from the National Automotive Sampling System - General Estimating System (NASS-GES) data were utilized to form a crash sequence: critical event, crash types, and sequence of events. A mixed-effects model was constructed to model the severity levels for each crash sequence while accounting for the effects of those crash contributing factors on crash severity. A total of 8039 crashes involving 8208 motorcycles occurred during 2011 and 2013 were included in this study, weighted to represent 338,655 motorcyclists involved in traffic crashes in three years (2011–2013)(NHTSA, 2013). The top five most frequent and severe types of crash sequences were identified, accounting for 23 percent of all the motorcycle crashes included in the study, and they are (1) run-off-road crashes on the right, and hitting roadside objects, (2) cross-median crashes, and rollover, (3) left-turn oncoming crashes, and head-on, (4) crossing over (passing through) or turning into opposite direction at intersections, and (5) side-impacted. In addition to crash sequences, several other factors were also identified to have effects on crash severity: use of helmet, presence of horizontal curves, alcohol consumption, road surface condition, roadway functional class, and nighttime condition.</description><identifier>ISSN: 0001-4575</identifier><identifier>EISSN: 1879-2057</identifier><identifier>DOI: 10.1016/j.aap.2018.03.022</identifier><identifier>PMID: 29627710</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Accidents, Traffic - classification ; Alcohol Drinking ; Environment ; Head Protective Devices - statistics & numerical data ; Humans ; Injury Severity Score ; Motorcycle crashes ; Motorcycles ; Pilot Projects ; Risk ; Risk matrix ; Sequence of events ; Traffic safety ; Wounds and Injuries - etiology</subject><ispartof>Accident analysis and prevention, 2018-08, Vol.117, p.21-31</ispartof><rights>2018 Elsevier Ltd</rights><rights>Copyright © 2018 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c381t-7bd6b17d531410c6dd9871a28fb2cba0970928968e3b4b73a9ba9dbe0f68466a3</citedby><cites>FETCH-LOGICAL-c381t-7bd6b17d531410c6dd9871a28fb2cba0970928968e3b4b73a9ba9dbe0f68466a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S000145751830126X$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29627710$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wu, Kun-Feng</creatorcontrib><creatorcontrib>Sasidharan, Lekshmi</creatorcontrib><creatorcontrib>Thor, Craig P.</creatorcontrib><creatorcontrib>Chen, Sheng-Yin</creatorcontrib><title>Crash sequence based risk matrix for motorcycle crashes</title><title>Accident analysis and prevention</title><addtitle>Accid Anal Prev</addtitle><description>•This study constructs a crash sequence based risk matrix to identify most frequent and most severe motorcycle crashes.•The information on crash sequences significantly improves the model goodness-of-fit in the crash severity models.•Five most frequent and most severe motorcycle crashes in the US were identified.
Considerable research has been conducted related to motorcycle and other powered-two-wheeler (PTW) crashes; however, it always has been controversial among practitioners concerning with types of crashes should be first targeted and how to prioritize resources for the implementation of mitigating actions. Therefore, there is a need to identify types of motorcycle crashes that constitute the greatest safety risk to riders - most frequent and most severe crashes. This pilot study seeks exhibit the efficacy of a new approach for prioritizing PTW crash causation sequences as they relate to injury severity to better inform the application of mitigating countermeasures. To accomplish this, the present study constructed a crash sequence-based risk matrix to identify most frequent and most severe motorcycle crashes in an attempt to better connect causes and countermeasures of PTW crashes. Although the frequency of each crash sequence can be computed from crash data, a crash severity model is needed to compare the levels of crash severity among different crash sequences, while controlling for other factors that also have effects on crash severity such drivers’ age, use of helmet, etc. The construction of risk matrix based on crash sequences involve two tasks: formulation of crash sequence and the estimation of a mixed-effects (ME) model to adjust the levels of severities for each crash sequence to account for other crash contributing factors that would have an effect on the maximum level of crash severity in a crash. Three data elements from the National Automotive Sampling System - General Estimating System (NASS-GES) data were utilized to form a crash sequence: critical event, crash types, and sequence of events. A mixed-effects model was constructed to model the severity levels for each crash sequence while accounting for the effects of those crash contributing factors on crash severity. A total of 8039 crashes involving 8208 motorcycles occurred during 2011 and 2013 were included in this study, weighted to represent 338,655 motorcyclists involved in traffic crashes in three years (2011–2013)(NHTSA, 2013). The top five most frequent and severe types of crash sequences were identified, accounting for 23 percent of all the motorcycle crashes included in the study, and they are (1) run-off-road crashes on the right, and hitting roadside objects, (2) cross-median crashes, and rollover, (3) left-turn oncoming crashes, and head-on, (4) crossing over (passing through) or turning into opposite direction at intersections, and (5) side-impacted. In addition to crash sequences, several other factors were also identified to have effects on crash severity: use of helmet, presence of horizontal curves, alcohol consumption, road surface condition, roadway functional class, and nighttime condition.</description><subject>Accidents, Traffic - classification</subject><subject>Alcohol Drinking</subject><subject>Environment</subject><subject>Head Protective Devices - statistics & numerical data</subject><subject>Humans</subject><subject>Injury Severity Score</subject><subject>Motorcycle crashes</subject><subject>Motorcycles</subject><subject>Pilot Projects</subject><subject>Risk</subject><subject>Risk matrix</subject><subject>Sequence of events</subject><subject>Traffic safety</subject><subject>Wounds and Injuries - etiology</subject><issn>0001-4575</issn><issn>1879-2057</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kMtOwzAQRS0EoqXwAWxQlmwSxnbih1ihipdUiQ2sLb8iXJqm2Cmif4-jFpasRiOdezVzELrEUGHA7GZZab2pCGBRAa2AkCM0xYLLkkDDj9EUAHBZN7yZoLOUlnnlgjenaEIkI5xjmCI-jzq9F8l_bv3a-sLo5F0RQ_ooOj3E8F20fSy6fuij3dmVL-zI-3SOTlq9Sv7iMGfo7eH-df5ULl4en-d3i9JSgYeSG8cM5q6huMZgmXNScKyJaA2xRoPkIImQTHhqasOplkZLZzy0TNSMaTpD1_veTezziWlQXUjWr1Z67fttUgQIrfNbkmcU71Eb-5Sib9Umhk7HncKgRl9qqbIvNfpSQFX2lTNXh_qt6bz7S_wKysDtHvD5ya_go0o2jKZciN4OyvXhn_ofMHF6Ig</recordid><startdate>20180801</startdate><enddate>20180801</enddate><creator>Wu, Kun-Feng</creator><creator>Sasidharan, Lekshmi</creator><creator>Thor, Craig P.</creator><creator>Chen, Sheng-Yin</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20180801</creationdate><title>Crash sequence based risk matrix for motorcycle crashes</title><author>Wu, Kun-Feng ; Sasidharan, Lekshmi ; Thor, Craig P. ; Chen, Sheng-Yin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c381t-7bd6b17d531410c6dd9871a28fb2cba0970928968e3b4b73a9ba9dbe0f68466a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Accidents, Traffic - classification</topic><topic>Alcohol Drinking</topic><topic>Environment</topic><topic>Head Protective Devices - statistics & numerical data</topic><topic>Humans</topic><topic>Injury Severity Score</topic><topic>Motorcycle crashes</topic><topic>Motorcycles</topic><topic>Pilot Projects</topic><topic>Risk</topic><topic>Risk matrix</topic><topic>Sequence of events</topic><topic>Traffic safety</topic><topic>Wounds and Injuries - etiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Kun-Feng</creatorcontrib><creatorcontrib>Sasidharan, Lekshmi</creatorcontrib><creatorcontrib>Thor, Craig P.</creatorcontrib><creatorcontrib>Chen, Sheng-Yin</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Accident analysis and prevention</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Kun-Feng</au><au>Sasidharan, Lekshmi</au><au>Thor, Craig P.</au><au>Chen, Sheng-Yin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Crash sequence based risk matrix for motorcycle crashes</atitle><jtitle>Accident analysis and prevention</jtitle><addtitle>Accid Anal Prev</addtitle><date>2018-08-01</date><risdate>2018</risdate><volume>117</volume><spage>21</spage><epage>31</epage><pages>21-31</pages><issn>0001-4575</issn><eissn>1879-2057</eissn><abstract>•This study constructs a crash sequence based risk matrix to identify most frequent and most severe motorcycle crashes.•The information on crash sequences significantly improves the model goodness-of-fit in the crash severity models.•Five most frequent and most severe motorcycle crashes in the US were identified.
Considerable research has been conducted related to motorcycle and other powered-two-wheeler (PTW) crashes; however, it always has been controversial among practitioners concerning with types of crashes should be first targeted and how to prioritize resources for the implementation of mitigating actions. Therefore, there is a need to identify types of motorcycle crashes that constitute the greatest safety risk to riders - most frequent and most severe crashes. This pilot study seeks exhibit the efficacy of a new approach for prioritizing PTW crash causation sequences as they relate to injury severity to better inform the application of mitigating countermeasures. To accomplish this, the present study constructed a crash sequence-based risk matrix to identify most frequent and most severe motorcycle crashes in an attempt to better connect causes and countermeasures of PTW crashes. Although the frequency of each crash sequence can be computed from crash data, a crash severity model is needed to compare the levels of crash severity among different crash sequences, while controlling for other factors that also have effects on crash severity such drivers’ age, use of helmet, etc. The construction of risk matrix based on crash sequences involve two tasks: formulation of crash sequence and the estimation of a mixed-effects (ME) model to adjust the levels of severities for each crash sequence to account for other crash contributing factors that would have an effect on the maximum level of crash severity in a crash. Three data elements from the National Automotive Sampling System - General Estimating System (NASS-GES) data were utilized to form a crash sequence: critical event, crash types, and sequence of events. A mixed-effects model was constructed to model the severity levels for each crash sequence while accounting for the effects of those crash contributing factors on crash severity. A total of 8039 crashes involving 8208 motorcycles occurred during 2011 and 2013 were included in this study, weighted to represent 338,655 motorcyclists involved in traffic crashes in three years (2011–2013)(NHTSA, 2013). The top five most frequent and severe types of crash sequences were identified, accounting for 23 percent of all the motorcycle crashes included in the study, and they are (1) run-off-road crashes on the right, and hitting roadside objects, (2) cross-median crashes, and rollover, (3) left-turn oncoming crashes, and head-on, (4) crossing over (passing through) or turning into opposite direction at intersections, and (5) side-impacted. In addition to crash sequences, several other factors were also identified to have effects on crash severity: use of helmet, presence of horizontal curves, alcohol consumption, road surface condition, roadway functional class, and nighttime condition.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>29627710</pmid><doi>10.1016/j.aap.2018.03.022</doi><tpages>11</tpages></addata></record> |
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| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | Accidents, Traffic - classification Alcohol Drinking Environment Head Protective Devices - statistics & numerical data Humans Injury Severity Score Motorcycle crashes Motorcycles Pilot Projects Risk Risk matrix Sequence of events Traffic safety Wounds and Injuries - etiology |
| title | Crash sequence based risk matrix for motorcycle crashes |
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