Urban road traffic condition detection method based on voting of network sorter

Abstract

The invention relates to an urban road traffic condition detection method based on the voting of a network sorter, which comprises the following steps: 1) monitoring traffic characteristic parameters in real time, and extracting the traffic characteristic parameters so as to obtain test sample sets, wherein the traffic characteristic parameters comprises average vehicle speed v(m / s), vehicle flow f(veh / s), time occupancy ratio s, and travel time t(s); 2) respectively carrying out classification by a SVM (support vector machine)classifier, a BP(beeper) neural network, and a SVM-BP (support vector machine-beeper) cascade classifier in the method of combining the voting of the SVM classifier, a BP neural network, and a SVM-BP cascade classifier; if the three categories are same, sorting the test samples into the category; or if two categories are same, comparing the sum of two weights of the same category with the weight of different categories so as to determine large weights as the classification results; or if the three categories are different, taking the results recognized by the classifiers with the highest weights as the results after the mergence. The urban road traffic condition detection method provided by the invention can efficiently enhance the accuracy.

Description

technical field [0001] The invention relates to a method for detecting urban road traffic state. Background technique [0002] The most prominent problems in the operation and management of urban road traffic are traffic congestion and traffic accidents. By studying the traffic state detection algorithm, the negative effects of traffic incidents on highway operation can be reduced. Through the rapid detection of traffic status, and the use of traffic flow guidance, traffic control and other means, the adverse impact of traffic congestion on road network operation can be minimized globally, the expansion of congestion can be avoided, and the safety and comfort of vehicles can be ensured. drive. [0003] Researchers at home and abroad have done some research on the traffic status discrimination of urban roads and expressways. The earliest automatic incident detection algorithm used was the California algorithm. The method judges possible sudden traffic incidents by compari...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of the poor accuracy of existing urban road traffic state detection methods, the present invention provides an urban road traffic state detection method based on multiple neural network classifier voting that effectively improves accuracy

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