A data-driven rule-based system for China's traffic accident prediction by considering the improvement of safety efficiency. (February 2023) - Ye, Fei-Fei; Yang, Long-Hao; Wang, Ying-Ming; Lu, Haitian
Highlights: A hybrid model regarding safety efficiency evaluation and traffic accident prediction is proposed for the first time. Meta-frontier and group-frontier are considered to improve the reasonability of the proposed hybrid model. The efficiency is used together with historical data to construct EBRBS to predict the future number of traffic accidents. Experimental results in China transportation management demonstrate the proposed model. Abstract: Rapid traffic development brings convenience to social circulation, but the number of fatalities in traffic accidents has brought great pressure on traffic safety and social stability management. Therefore, traffic accidents prediction is being of great significance to alleviate the safety pressure of regional traffic management. Nevertheless, the existing studies has yet reached a consensus on the scientific and feasible modeling method for traffic safety management, the improvement of traffic safety efficiencies has also rarely discussed in traffic accidents prediction. This paper fills the gap by promoting a novel data-driven decision model for traffic accidents prediction, which is constructed by the extended belief rule-based system (EBRBS) with considering the improvement of traffic safety efficiencies. Hence, the new traffic accident prediction model consists of two components: 1) safety efficiencies evaluation modeling with considering meta-frontier and group-frontier to evaluate the current traffic safety management, which are also defined to improve safety efficiencies evaluation by the adjustment of inputs and outputs; 2) extended belief rule base (EBRB)-based modeling for traffic accidents prediction by considering the improvement of traffic safety efficiencies, where the effective efficiencies of traffic management inputs and outputs are utilized to predict the future number of traffic accidents. The effectiveness of the proposed model is verified by using traffic management data from 31 Chinese provinces during 2003–2020. Experimental results demonstrate that the model can offer powerful reference value in the traffic accidents prediction process, which help to achieve the relatively effective efficiencies of traffic safety.