Motor vehicle driver injury severity analysis utilizing a random parameter binary probit model considering different types of driving licenses in 4-legs roundabouts in South Australia

Zubaidi, Hamsa Abbas, Obaid, Ihsan Ali, Alnedawi, Ali and Das, Subasish 2021, Motor vehicle driver injury severity analysis utilizing a random parameter binary probit model considering different types of driving licenses in 4-legs roundabouts in South Australia, Safety Science, vol. 134, doi: 10.1016/j.ssci.2020.105083.

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Title Motor vehicle driver injury severity analysis utilizing a random parameter binary probit model considering different types of driving licenses in 4-legs roundabouts in South Australia
Author(s) Zubaidi, Hamsa Abbas
Obaid, Ihsan Ali
Alnedawi, Ali
Das, Subasish
Journal name Safety Science
Volume number 134
Article ID 105083
Total pages 10
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2021-02
ISSN 0925-7535
Keyword(s) injury severity
roundabout
random parameter
binary probit
driver experience
Summary A roundabout may not provide an acceptable level of control and can be confusing to inexperienced drivers. Therefore, the purpose of this study is to identify the contributing factors that lead to specific driver injury severity by utilizing a random parameter binary probit model sustained by different experiences of motor drivers at 4-legs roundabouts in South Australia. Four models were estimated based on seven years of crash data (2012–2018), considering different types of motorist-driving license: learner, provisional, full, and for all datasets, including unknown licensures. The model estimates variables have been categorized into a driver, crash, temporal, spatial, vehicle, roadway characteristics, and vehicle movements. The results showed there are differences between resulting crash-injury severities when driver experience has been observed. Besides, several parameters were found to be random and normally distributed: safety equipment, crash type (rear-end crash), number of involved vehicles, weekdays indicator, stats area (crash occurred within metropolitan), vehicle type (passenger car), and posted speed limit (more than 50 km/hr.). In addition, the log-likelihood and the transferability test indicated that the data should be separated and analyzed according to the driver's license. Findings can help authorities to improve driver safety considering the influence of the driver experience.
Language eng
DOI 10.1016/j.ssci.2020.105083
Indigenous content off
Field of Research 09 Engineering
11 Medical and Health Sciences
17 Psychology and Cognitive Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2020, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30145021

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