A traffic state prediction method for urban road network based on lstm-cnn

Abstract

The present invention is oriented to the prediction of the traffic state of the urban road network, and will be described with the vehicle speed of the road section as the prediction object. First, the road network adjacency matrix is ​​filled with the speed of the road section to represent the traffic state of the road network in a certain period of time, and the spatial characteristics of the traffic state are learned through the convolutional neural network, and then the long-short-term memory neural network is used to receive the spatial characteristics of different periods of time, and the characteristics of the time dimension are introduced. Learning, and finally combine the two kinds of feature information to predict the average travel speed of each section of the road network. The present invention proposes LSTM-CNN based on the road network adjacency matrix, which can better grasp the time-space nonlinear relationship of the road network traffic state compared with the traditional statistical method. Compared with the current deep learning method, the road network adjacency matrix based The traffic state input of the road network, while retaining the spatial characteristics of the road network traffic state, reduces the input of redundant information, reduces, passes, and effectively learns the characteristics of the road network traffic state, which has a better prediction effect.

Description

technical field

[0001] The present invention relates to the field of intelligent transportation, and more specifically, relates to an LSTM-CNN-based urban road network traffic state prediction method. Background technique

[0002] In recent years, urban traffic congestion has occurred frequently and has become common in economically developed cities. In order to improve road operating conditions and improve residents' living standards, the development of urban intelligent transportation systems (Intelligent Transportation System, referred to as ITS) has become an urgent need for urban development. With the continuous improvement of information, sensing, communication, and computer technologies, as a product of the comprehensive application of these technologies, ITS is in a stage of rapid development, playing an increasingly important role in allocating urban road resources and improving road network traffic efficiency character of. Accurate prediction of the traffic status...

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