Urban road traffic condition detection method based on neural network classifier cascade fusion

Abstract

The invention relates to an urban road traffic condition detection method based on the neural network classifier cascade fusion, 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) inputting the test sample sets into a bilayered SVM-BP (support vector machine-beeper) cascade classifier, wherein the step of inputting the test sample sets into the bilayered SVM-BP (support vector machine-beeper) cascade classifier comprises the steps: 2.1) judging whether the urban road traffic condition belongs to the unblocked status or not by inputting SVM (support vector machine) training functions and data of the test sample sets into a SVM classification functions after SVM (support vector machine) training functions are respectively trained; if so, judging that the existing status belongs to the unblocked status, or if not, executing the step 2.2; and 2.2) processing the test sample sets, and testing the test sample sets so as to judge whether the urban road traffic condition belongs to the unblocked status or not by utilizing the BP neural network. 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 neural network classifier cascade fusion that effectively improves accuracy

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