Urban road traffic state detection method combined with support vector machine (SVM) and back propagation (BP) neural network

Abstract

The invention discloses an urban road traffic state detection method combined with a support vector machine (SVM) and a back propagation (BP) neural network. The method comprises the following steps of: 1) monitoring traffic characteristic parameters of a road section in real time, and extracting the traffic characteristic parameters to obtain a test sample set, wherein the traffic characteristic parameters comprise a vehicle average speed v (m/s), a vehicle flow size f (veh/s), time occupancy s and travel time t (s); and 2) inputting the test sample set into two layers of cascade classifiers of SVM1-SVM2/BP, wherein the step 2) comprises the following substeps of: 2.1) training the two layers of cascade classifiers by applying an SVM1 training function, and inputting into an SVM1 classification function together with test sample data, judging whether the SVM1 classification function is in a smooth state, if so, determining that the current state is the smooth state, otherwise, entering the substep 2.2); and 2.2) performing vote combination classification on the test sample set by the second layer of SVM2 and BP network classifier, and judging whether the test sample set is in a busy state or a congestion state. By the method, the accuracy can be effectively improved.

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 deficiencies of poor accuracy of existing urban road traffic state detection methods, the present invention provides an urban road traffic state detection method that effectively improves accuracy by integrating SVM and BP neural network

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