SVM-Based Real-Time Identification Model of Dangerous Traffic Stream State

Abstract

By comparing and studying the correlation between traffic stream parameters and traffic safety of different highways, the correlations of traffic natural quantity, traffic equivalent, passenger-cargo ratio, car following percentage, congestion degree, and time occupancy rate are obtained. The traffic stream state before the actual accident is used as the criterion to judge the bad traffic stream state. The main parameters are obtained by extracting the parameters from the traffic stream data at the lane level and reducing the dimension of the parameters with the principal component analysis method. Establish a SVM model for RT early warning of traffic stream safety. Compared with other methods, the adaptive parameter selection method can adaptively select parameters according to the training sample set, realize the adaptive ability of the forecast model, and effectively improve the forecast accuracy of traffic stream. This paper studies the risk early warning model of road traffic accidents, which can transform the problem of road traffic safety into active early warning and improve the level of traffic safety. This study provides safety management measures for highway operation departments, which has certain theoretical significance and practical application value.

Improving Detection and Prediction of Traffic Congestion in VANETs: An Examination of Machine Learning

“…Furthermore, in a recent study by Huang (2022) [25], the focus was on crafting an SVM-Based Real-Time Identification Model for road traffic accidents. This research sought to transform road traffic safety concerns into an active early warning system, offering risk assessment and management strategies for highway operation authorities.…”