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

Ming Huang,Xin Ning

doi:10.1155/2022/6260395

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.

Highlights

With the rapid development of social economy, the number of cars is increasing linearly, and the increasing number of private cars is convenient for citizens to travel, and brings about problems such as traffic congestion and environmental pollution [1]
Based on the influence of traffic stream on traffic safety, this paper fully excavates the potential laws and characteristics between traffic accidents and traffic stream characteristics of expressway and constructs a RT Traffic accident risk early warning model based on traffic stream, which provides safety management measures for expressway traffic management
Based on the research of parallel technology, parallel algorithms are introduced for traffic stream forecasting, and parallel experiments are conducted on multiple road sections at the same time. rough simulation experiments, the constructed Support Vector Machine (SVM) model can successfully identify the bad traffic stream state corresponding to most accidents and can effectively monitor and warn the bad traffic stream state on the highway in real time. e practicability and usability of the forecast of this model are verified

Summary

Introduction

With the rapid development of social economy, the number of cars is increasing linearly, and the increasing number of private cars is convenient for citizens to travel, and brings about problems such as traffic congestion and environmental pollution [1]. Urban road traffic state recognition analyzes various traffic data using various discrimination algorithms, compares the results to prior traffic state standards, determines the current traffic system operation state, and implements intelligent traffic control, management, and guidance based on the discrimination results. Realizing fast and effective traffic state recognition is an important guarantee for realizing RT intelligent and effective control of urban traffic [12]. It is the goal of this paper to introduce the idea of active early warning control into traffic safety management, build a RT and efficient early warning model of traffic accident risk, reduce the occurrence of highway traffic accidents, and provide safe and efficient experience for highway travelers. Based on the influence of traffic stream on traffic safety, this paper fully excavates the potential laws and characteristics between traffic accidents and traffic stream characteristics of expressway and constructs a RT Traffic accident risk early warning model based on traffic stream, which provides safety management measures for expressway traffic management

Related Work

Methodology

Analysis and Discussion

Findings

Conclusions