A multinomial logit model of motorcycle crash severity at Australian intersections |
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| Institution: | 1. School of Civil Engineering, College of Engineering, University of Tehran, Iran;2. School of Engineering, RMIT University, Melbourne, Australia;1. The George Institute for Global Health, Sydney, NSW, Australia;2. School of Public Health & Community Medicine, UNSW, Sydney, NSW, Australia;3. Centre for Accident Research and Road Safety – Queensland (CARRS-Q), Queensland University of Technology (QUT), Brisbane, Queensland, Australia;4. University of Technology Sydney (UTS), Graduate School of Health, Sydney, NSW, Australia;5. Global Road Safety Solutions (GRSS) Pty Ltd, NSW, Australia;6. Deakin University Institute for Frontier Materials, Geelong, Victoria, Australia;7. School of Aviation, Transport and Road Safety (TARS) Research, Faculty of Science, UNSW, Sydney, NSW, Australia;8. Ngarruwan Ngadju, Australian Health Services Research Institute, University of Wollongong, Wollongong, NSW, Australia;1. Department of Civil Engineering, California State Polytechnic University, Pomona, 3801 W. Temple Ave., Pomona, CA, 91768, United States;2. California Department of Public Health, Sacramento, CA, 95899-7377, United States;1. Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Melbourne, Victoria, Australia;2. Institute of Construction Engineering, University of Transport and Communications, Hanoi, Vietnam;3. School of Business IT & Logistics, RMIT University, Melbourne, Victoria, Australia;1. Institute for Transportation, Iowa State University, 2711 South Loop Drive, Suite 4700, Ames, IA 50010, United States;2. Lyles School of Civil Engineering, HAMP G167B, Purdue University, West Lafayette, IN 47907, United States;1. Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, China;3. Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China |
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| Abstract: | Introduction: Motorcyclists are exposed to more fatalities and severe injuries per mile of travel as compared to other vehicle drivers. Moreover, crashes that take place at intersections are more likely to result in serious or fatal injuries as compared to those that occur at non-intersections. Therefore, the purpose of this study is to evaluate the contributing factors to motorcycle crash severity at intersections. Method: A data set of 7,714 motorcycle crashes at intersections in the State of Victoria, Australia was analyzed over the period of 2006–2018. The multinomial logit model was used for evaluating the motorcycle crashes. The severity of motorcycle crashes was divided into three categories: minor injury, serious injury and fatal injury. The risk factors consisted of four major categories: motorcyclist characteristics, environmental characteristics, intersection characteristics and crash characteristics. Results: The results of the model demonstrated that certain factors increased the probability of fatal injuries. These factors were: motorcyclists aged over 59 years, weekend crashes, midnight/early morning crashes, morning rush hours crashes, multiple vehicles involved in the crash, t-intersections, crashes in towns, crashes in rural areas, stop or give-way intersections, roundabouts, and uncontrolled intersections. By contrast, factors such as female motorcyclists, snowy or stormy or foggy weather, rainy weather, evening rush hours crashes, and unpaved roads reduced the probability of fatal injuries. Practical Applications: The results from our study demonstrated that certain treatment measures for t-intersections may reduce the probability of fatal injuries. An effective way for improving the safety of stop or give-way intersections and uncontrolled intersections could be to convert them to all-way stop controls. Further, it is recommended to educate the older riders that with ageing, there are physiological changes that occur within the body which can increase both crash likelihood and injury severity. |
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| Keywords: | Multinomial logit model Injury severity Motorcycle crashes Intersection Risk factors |
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