|Institution:||University of Dayton|
|Keywords:||Civil Engineering; Transportation; Motorcycle; Influential Factors; Traffic Crash; Logistic Regression; Stepwise Selection; Traffic Safety|
|Full text PDF:||http://rave.ohiolink.edu/etdc/view?acc_num=dayton1406031500|
As everybody knows, there are many traffic crashes happening every day. Traffic crashes may result in injury, death, and property damage. A number of factors contribute to the risk of a crash, including vehicle design, speed of operation, road design, road environment, driver skill and/or impairment, and driver behavior. Worldwide, motor vehicle crashes lead to death and disability as well as financial costs to both society and the individuals involved. The objective of this study was to analyze crash data of motorcycle-motor vehicle collisions to identify possibly influential factors that cause these crashes and to study the magnitude of influence of each factor to these crashes. This study tested appropriate regression models to accurately model the factors that significantly influence motorcycle-motor vehicle crashes. A nominal multinomial logistic regression model was built. From stepwise selection procedure, the influential factors included age, time of crash, number of units, vehicle in error, road contour, collision type, alcohol used, posted speed, and helmet used. Number of units involved in a crash impacts the crash severity level, such as two units mostly result in injury and three or more units mostly result in fatal. If the driver of the motor vehicle causes the crash it will more likely result into injury than if the driver of the motorcycle causes the crash. Driver of motorcycle or vehicle that uses alcohol will certainly increase the chance of a fatality or injury. Crashes that occur on highways or freeways with higher speed limits are more likely to result in injuries and fatalities. The occupants of motorcycle use helmet will significantly be protected in the crash. These factors can be applied to reduce the severity of motorcycle-motor vehicle crashes.