Traffic state estimation method based on clustering and deep sequence learning

Abstract

The invention, which belongs to the field of intelligent traffic systems, provides a traffic state estimation method based on kmeans clustering and deep sequence learning, thereby solving the problemthat the traffic state of a whole expressway cannot be estimated under the condition that traffic flow data of part of road sections in the urban expressway cannot be acquired in real time. The methodis characterized by comprising the following steps: (1), dividing an expressway network; (2), carrying out modeling and data acquisition of an expressway; (3), preprocessing and normalizing the data;(4), calculating the Euclidean distance between the traffic flow data through a kmeans clustering algorithm, and determining the traffic state grade of each data point; and (5), designing a deep sequence learning Seq2Seq model, and carrying out traffic state identification on the whole road network through model iterative learning. The method gives full consideration to the relation of traffic flows between road segments and gives play to the advantages of a machine learning algorithm in the traffic field; the traffic state of the whole road network can be obtained in time; and reliable traffic information can be provided for a driving main body.

Description

technical field [0001] The invention relates to the field of intelligent traffic systems, in particular to a traffic state estimation method based on kmeans clustering and deep sequence learning. Background technique [0002] With the continuous development of my country's social economy and the continuous growth of urban population, more and more families have one or more private cars. The rapid increase in the number of vehicles has led to increasing traffic pressure in major cities in my country, seriously affecting the The operating efficiency of the urban traffic road network increases the travel time of residents. In addition, low-speed driving of vehicles in congestion will aggravate energy waste, and frequent flameouts and starts will also increase exhaust emissions and pollute the living environment of residents. Therefore, how to accurately estimate the traffic flow of the urban road network and alleviate the traffic pressure under the condition of meeting people's ...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to address the above existing problems, to provide a traffic state estimation method based on kmeans clustering and deep sequence learning, aiming to solve the problem that the traffic flow data of some road sections in the urban expressway cannot be obtained in real time. The problem of estimating the traffic state of road segments

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