Abstract
Run-off-road (ROR) accidents cause a large proportion of fatalities on roads. Exploring key factors is an effective method to reduce fatalities and improve safety sustainability. However, some limitations exist in current studies: (1) Datasets of ROR accidents have imbalance problems, in which the samples of fatal accidents (FA) are much less than non-fatal accidents (NFA). Data mining methods on such imbalanced datasets make the results biased. (2) Few studies conducted spatial analysis of ROR accidents in visualization. Therefore, this study proposes an association rule mining (ARM)-based framework to analyze ROR accidents on imbalanced datasets. A novel method is proposed to address the imbalance problem and ARM is applied to analyze accident severity. Geographic information system (GIS) is adopted for spatial analysis of ROR accidents. The proposed framework is applied to ROR accidents in Victoria, Australia. Six FA factors and seven NFA factors are identified from two-item rules. The results of three-item rules indicate factors acting interactively increase the likelihood of FA or NFA. Hot spots of ROR accidents are presented by GIS maps. Effective measures are accordingly proposed to improve road safety. Compared with traditional data-balancing methods, the proposed framework has been validated to provide more robust and reliable results on imbalanced datasets.
Highlights
Road traffic accidents are considered as a public safety problem globally [1,2]
To fill the research gap, this study aims to apply Geographic information system (GIS) for spatial analysis to identify hot spots of ROR accidents related to key factors
To fill the above research gaps and limitations, this study proposes a framework to explore key factors associated with accident severity on imbalanced datasets of ROR accidents, especially the key factors of fatal accidents
Summary
Road traffic accidents are considered as a public safety problem globally [1,2]. They cause great casualties, economic losses, and traffic congestion each year [3,4]. The World Health Organization (WHO) reported that over 1.35 million people died, and 50 million people were injured each year due to traffic crashes [5]. The economic cost of traffic crashes was estimated as 3% of gross domestic product (GDP) globally, and up to 5% for low-income and middle-income countries [5]. Among all the traffic accident types, run-off-road (ROR) accidents are an important subset of traffic accidents because they yield most of the fatalities and severe injuries on roads. Existing studies on ROR accidents are still very limited
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
