Towards efficient and accurate prediction of freeway accident severity using two-level fuzzy comprehensive evaluation

Accurately predicting freeway accident severity is crucial for accident prevention, road safety, and emergency rescue services in intelligent freeway systems. However, current research lacks the required precision, hindering the effective implementation of freeway rescue. In this paper, we efficiently address this challenge by categorizing influencing factors into two levels: human and non-human, further subdivided into 6 and 36 categories, respectively. Furthermore, based on the above factors, an efficient and accurate Freeway Accident Severity Prediction (FASP) method is developed by using the two-level fuzzy comprehensive evaluation. The factor and evaluation sets are determined by calculating the fuzzy evaluation matrix of a single factor. The weight matrix is calculated through the entropy method to compute the final evaluation matrix. Based on the maximum membership principle, the severity of the freeway accident is predicted. Finally, based on the experiments conducted with the traffic accident datasets in China and the US, it is shown that FASP is able to accurately predict the severity of freeway traffic accidents with thorough considerations and low computational cost. It is noted that FASP is the first attempt to achieve freeway accident severity prediction using the two-level fuzzy comprehensive evaluation method to the best of our knowledge.