Method for detecting freeway traffic event by integration supporting vector machine

Abstract

The invention relates to a method of integrated support vector machine for detecting a traffic incident in an express highway, which can improve a detectability of a detecting system greatly, and avoid an overhead time added due to a training parameter optimization of the support vector machine. The method adopts a Boosting or Bagging method to train a plurality of support vector machines; each sample for training the support vector machine comprises a traffic flow, an occupancy, a speed and a traffic state of an upstream check station and a downstream check station in a detecting area; a value 1 or -1 is used for the representation of traffic state to represent respectively an incident occurring or a non incident occurring; each of the support vector machine is used for judging whether the traffic incident occurs; and the judgment results of the support vector machines are synthesized through a majority voting or a weighted voting so as to judge the present traffic state in the detecting area of the express highway according to the synthesized results, thereby detecting the occurrence of the incident.

Description

technical field [0001] Based on the support vector machine and integrated learning technology, the present invention proposes a method for automatically detecting expressway traffic incidents through the integrated support vector machine, and relates to traffic intelligent management and control technology. Background technique [0002] my country's traffic problems are becoming more and more serious, with frequent traffic incidents and serious traffic congestion. The resulting loss of productivity, property loss, and personal injury have reached hundreds of millions of yuan, which has seriously affected the sustainable development of cities and the safety of people's lives and properties. become an urgent social problem. Automatic Incident Detection (AID) is an important part of modern traffic monitoring system, which is of great significance to road traffic safety. Since the 1960s, the research on automatic detection of traffic incidents has attracted the attention of traf...

AI Technical Summary

Problems solved by technology

However, the kernel function of SVM and its parameters have a great influence on the classification performance. How to choose the appropriate kernel function and its parameters is a challenging task. At present, it is generally explored through a lot of laborious and time-consuming experiments. Therefore, the application of SVM The effect depends entirely on the user's experience, which affects its ability to detect traffic events and limits its application

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