Browsing by Author "Thor, Craig P."

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Crash sequence based risk matrix for motorcycle crashes
(2018-08-01)
Wu, Kun-Feng
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Sasidharan, Lekshmi
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Thor, Craig P.
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Chen, Sheng-Yin
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運輸與物流管理系註：原交通所+運管所
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-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.
Identify sequence of events likely to result in severe crash outcomes
(2016-11-01)
Wu, Kun-Feng
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Thor, Craig P.
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Ardiansyah, Muhammad Nashir
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
The current practice of crash characterization in highway engineering reduces multiple dimensions of crash contributing factors and their relative sequential connections, crash sequences, into broad definitions, resulting in crash categories such as head-on, sideswipe, rear-end, angle, and fixed-object. As a result, crashes that are classified in the same category may contain many different crash sequences. This makes it difficult to develop effective countermeasures because these crash categorizations are based on the outcomes rather than the preceding events. Consequently, the efficacy of a countermeasure designed for a specific type of crash may not be appropriate due to different pre-crash sequences. This research seeks to explore the use of event sequence to characterize crashes. Additionally, this research seeks to identify crash sequences that are likely to result in severe crash outcomes so that researchers can develop effective countermeasures to reduce severe crashes. This study utilizes the sequence of events from roadway departure crashes in the Fatality Analysis Reporting System (FARS), and converts the information to form a new categorization called "crash sequences." The similarity distance between each pair of crash sequences were calculated using the Optimal Matching approach. Cluster analysis was applied to group crash sequences that are etiologically similar in terms of the similarity distance. A hybrid model was constructed to mitigate the potential sample selection bias of FARS data, which is biased toward more severe crashes. The major findings include: (1) in terms of a roadway departure crash, the crash sequences that are most likely to result in high crash severity include a vehicle that first crosses the median or centerline, runs-off-road on the left, and then collides with a roadside fixed-object; (2) seat-belt and airbag usage reduces the probability of dying in a roadway departure crash by 90%; and (3) occupants who are seated on the side of the vehicle that experience a direct impact are 2.6 times more likely to die in a roadway departure crash than those not seated on the same side of the vehicle where the impact occurs. (C) 2016 Elsevier Ltd. All rights reserved.
Method for the Use Naturalistic Driving Study Data to Analyze Rear-End Crash Sequences
(2015)
Wu, Kun-Feng
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Thor, Craig P.
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
Many factors are associated with crash risk, but their contribution to the progression of a crash is often unclear. Research on and understanding of the crash sequence are needed. One of the advantages of studying crash sequence is to identify the causative chain of crashes and, subsequently, the identification of effective countermeasures that may feasibly mitigate crash risk. An analogy to epidemiological studies is particularly useful: although researchers confirm that many factors are associated with an increase in the probability of myocardial infarction, there is still a need to research the disease process, the physiological process that promotes disease growth, and the possible interventions to treat or even prevent the disease. This study used data from the Integrated Vehicle-Based Safety System program. Two freeway rear-end events, including one crash and one near crash, with similar crash sequences were studied. The crash sequences of the two events were as follows: (a) the drivers maintained a temporal headway of less than 1 s at the beginning; (b) the traffic flow conditions were likely to cause misjudgments by the drivers and result in glances in inappropriate directions; (c) when the drivers were distracted, the leading vehicle decelerated; (d) the drivers did not decelerate accordingly and had their feet on the accelerator pedal; and (e) one event resulted in a crash and the other in a near crash. This study highlighted an approach for comparing and dissecting the differences in the crash sequence that led to the different outcomes.