Highway Risk Prediction and Factor Evaluation using Convolutional Neural Networks

In this paper, we study the highway risk classification and accidents prediction using machine learning models. First, highway facilities data are collected in the form of numbers and text. The facilities information is then processed by encoding and digitalization. Second, stochastic optimization algorithm is used to select features for modeling the risk. Supervised information is provided by human expert. Third, two types of customized convolutional neural networks are introduced to highway road risk modeling. Different feature combinations are evaluated. Finally, experiments are carried out for risk classification, accidents number regression and feature set evaluation. The results show that the proposed highway risk model is effective and may contribute considerably to road risk management. © 2022,IAENG International Journal of Computer Science. All Rights Reserved.