Data mining approach to model bus crash severity in Australia |
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| Institution: | 1. School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran;2. Mianeh Technical and Engineering Faculty, University of Tabriz, Tabriz, Iran;3. School of Engineering, RMIT University, Melbourne, Australia;1. College of Engineering, Civil Engineering Department University of Wasit, Kut, Iraq;2. Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall, West Lafayette, IN 47907-2051, USA;1. Dept. of Geography Education, University of Education, Winneba, Ghana;2. UHasselt- Hasselt University, Transportation Research Institute (IMOB), Agoralaan, 3590 Diepenbeek, Belgium;3. Vias Institute, Haachtsesteenweg 1405, 1130 Brussels, Belgium;1. University of Naples Federico II, Department of Civil, Architectural and Environmental Engineering, Via Claudio 21, 80125 Naples, Italy;2. University of Granada, TRYSE Research Group, Department of Civil Engineering, Spain;1. Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, United States;2. Beaman Distinguished Professor & Transportation Program Coordinator, Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, AL 37996, United States;3. Alabama Transportation Institute, The University of Alabama, Tuscaloosa, AL 35487, United States;4. The Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, United States;5. Virginia Department of Transportation, Richmond, VA 23219, United States |
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| Abstract: | Introduction: Buses are different vehicles in terms of dimensions, maneuverability, and driver's vision. Although bus traveling is a safe mode to travel, the number of annual bus crashes cannot be neglected. Moreover, limited studies have been conducted on the bus involved in fatal crashes. Therefore, identification of the contributing factors in the bus involved fatal crashes can reduce the risk of fatality. Method: Data set of bus involved crashes in the State of Victoria, Australia was analyzed over the period of 2006–2019. Clustering of crash data was accomplished by dividing them into homogeneous categories, and by implementing association rules discovery on the clusters, the factors affecting fatality in bus involved crashes were extracted. Results: Clustering results show bus crashes with all vehicles except motor vehicles and weekend crashes have a high rate of fatality. According to the association rule discovery findings, the factors that increase the risk of bus crashes with non-motor vehicles are: old bus driver, collision with pedestrians at signalized intersections, and the presence of vulnerable road users. Likewise, factors that increase the risk of fatality in bus involved crashes on weekends are: darkness of roads in high-speed zones, pedestrian presence at highways, bus crashes with passenger car by a female bus driver, and the occurrence of multi-vehicle crashes in high-speed zones. Practical Applications: The study provides a sequential pattern of factors, named rules that lead to fatality in bus involved crashes. By eliminating or improving one or all of the factors involved in rules, fatal bus crashes may be prevented. The recommendations to reduce fatality in bus crashes are: observing safe distances with the buses, using road safety campaigns to reduce pedestrians’ distracted behavior, improving the lighting conditions, implementing speed bumps and rumble strips in high-speed zones, installing pedestrian detection systems on buses and setting special bus lanes in crowded areas. |
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| Keywords: | Bus involved crashes Crash severity Public transport Clustering Association rule discovery |
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