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dc.contributor.authorWu, Kun-Fengen_US
dc.contributor.authorSasidharan, Lekshmien_US
dc.contributor.authorThor, Craig P.en_US
dc.contributor.authorChen, Sheng-Yinen_US
dc.date.accessioned2018-08-21T05:53:49Z-
dc.date.available2018-08-21T05:53:49Z-
dc.date.issued2018-08-01en_US
dc.identifier.issn0001-4575en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.aap.2018.03.022en_US
dc.identifier.urihttp://hdl.handle.net/11536/145196-
dc.description.abstractConsiderable 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-offroad 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.en_US
dc.language.isoen_USen_US
dc.subjectTraffic safetyen_US
dc.subjectRisk matrixen_US
dc.subjectMotorcycle crashesen_US
dc.subjectSequence of eventsen_US
dc.titleCrash sequence based risk matrix for motorcycle crashesen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.aap.2018.03.022en_US
dc.identifier.journalACCIDENT ANALYSIS AND PREVENTIONen_US
dc.citation.volume117en_US
dc.citation.spage21en_US
dc.citation.epage31en_US
dc.contributor.department運輸與物流管理系
註:原交通所+運管所
zh_TW
dc.contributor.departmentDepartment of Transportation and Logistics Managementen_US
dc.identifier.wosnumberWOS:000436888400003en_US
Appears in Collections:Articles