Multi-model traffic accident clearance time prediction framework

Accurate traffic accident clearance times prediction can help road managers make effective decisions and reduce property damage. This paper aims to develop a framework for traffic accident clearance time prediction and find the best prediction model. We propose a multi-model prediction framework for traffic accident severity. This framework consists of three parts: preprocessing of imbalanced data, variable selection and establishment of hybrid models: RF-SVM, RFBPNN, and RF-BN. Four highways in Shandong Province's traffic accident data are used as a case study in this paper. Based on the data used in this paper and previous literature exploration, three mixed models are constructed. Comparing the outcomes, we discover that the RF-SVM model has the highest prediction accuracy, up to 0.98, for the oversampled data set. This framework can be used to forecast the clearance time for traffic accidents, allowing for prompt emergency response and a reduction in fatalities and property damage.