Traffic state prediction method based on clustering analysis and Markov model

Abstract

The invention relates to a traffic state prediction method based on clustering analysis and Markov models. The method comprises the steps of selecting the traffic volume and the traffic occupancy rateas evaluation indexes of the traffic state of urban crossroads; performing clustering analysis on the traffic volume and the traffic occupancy rate by a K means clustering method according to a largenumber of traffic flow data collected by a traffic control center; determining a weight set of the traffic volume and the traffic occupancy rate by a method combining an entropy evaluation method andan analytic hierarchy method; determining a membership degree function of the traffic volume and the traffic occupancy rate by a linear analysis method, and obtaining a membership degree matrix; calculating a fuzzy comprehensive evaluation matrix; evaluating the existing traffic state according to the maximum membership degree principle; and predicating the future traffic state on the basis of aMarkov model according to the existing traffic state. The traffic state prediction method is applicable to practical cases; and the prediction precision is improved.

Description

technical field [0001] The present invention designs the technical field of intelligent traffic, especially relates to traffic state prediction method based on cluster analysis and Markov model Background technique [0002] With the continuous development of economy and technology, people have higher and higher requirements for travel traffic information, such as knowing the current traffic status of the road before travel, so that they can choose the most effective traffic route and travel mode. Therefore, traffic control and guidance The system has become a popular core topic in the research of intelligent transportation systems, and the key issue in realizing traffic control and guidance systems is real-time and accurate traffic state prediction, that is, how to effectively use real-time traffic data to predict traffic conditions. Regarding the evaluation of traffic status based on traffic parameters, it is more common to use multiple traffic parameters such as traffic vo...

AI Technical Summary

Problems solved by technology

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a traffic state prediction method based on cluster analysis and Markov model, aiming to solve the problem of complex selection of evaluation indicators or the need to process detector data and a large amount of calculation

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