Analysis of motorcycle accidents using association rule mining-based framework with parameter optimization and GIS technology |
| |
| Institution: | 1. Centre for Applied Health Research, St. Joseph’s Care Group, 580 North Algoma Street, Thunder Bay, ON P7B 5G4, Canada;2. Centre for Research on Safe Driving, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada;3. Northern Ontario School of Medicine, Human Sciences Division, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada;4. Department of Psychology, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada;5. Department of Health Sciences, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada;6. School of Nursing, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada;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 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 |
| |
| Abstract: | IntroductionAnalyzing key factors of motorcycle accidents is an effective method to reduce fatalities and improve road safety. Association Rule Mining (ARM) is an efficient data mining method to identify critical factors associated with injury severity. However, the existing studies have some limitations in applying ARM: (a) Most studies determined parameter thresholds of ARM subjectively, which lacks objectiveness and efficiency; (b) Most studies only listed rules with high parameter thresholds, while lacking in-depth analysis of multiple-item rules. Besides, the existing studies seldom conducted a spatial analysis of motorcycle accidents, which can provide intuitive suggestions for policymakers. Method: To address these limitations, this study proposes an ARM-based framework to identify critical factors related to motorcycle injury severity. A method for parameter optimization is proposed to objectively determine parameter thresholds in ARM. A method of factor extraction is proposed to identify individual key factors from 2-item rules and boosting factors from multiple-item rules. Geographic information system (GIS) is adopted to explore the spatial relationship between key factors and motorcycle injury severity. Results and conclusions: The framework is applied to a case study of motorcycle accidents in Victoria, Australia. Fifteen attributes are selected after data preprocessing. 0.03 and 0.7 are determined as the best thresholds of support and confidence in ARM. Five individual key factors and four boosting factors are identified to be related to fatal injury. Spatial analysis is conducted by GIS to present hot spots of motorcycle accidents. The proposed framework has been validated to have better performance on parameter optimization and rule analysis in ARM. Practical applications: The hot spots of motorcycle accidents related to fatal factors are presented in GIS maps. Policymakers can refer to those maps straightforwardly when decision making. This framework can be applied to various kinds of traffic accidents to improve the performance of severity analysis. |
| |
| Keywords: | Motorcycle Accidents Association Rule Mining (ARM) threshold determination Accurate and Efficient Classification Based on Multiple Class-Association Rules (CMAR) Key Factors Geographic Information System (GIS) |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
