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

A technology of traffic status and prediction method, applied in the field of intelligent transportation, can solve problems such as only considering the time dimension, achieve good prediction effect and reduce the effect of redundant information input

Active Publication Date: 2021-10-01
SUN YAT SEN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In order to solve the shortcomings of the prior art that cannot use the nonlinear relationship of traffic state evolution and only consider the characteristics of the time dimension, the present invention provides a traffic state prediction method for urban road networks based on LSTM-CNN

Method used

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

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Embodiment 1

[0069] like figure 1 , figure 2 , image 3 As shown, a LSTM-CNN-based urban road network traffic state prediction method includes the following steps:

[0070] A LSTM-CNN-based traffic state prediction method for urban road network, the specific steps are as follows:

[0071] Step S1: Estimate and calculate the historical vehicle speed of the road network;

[0072] Step S2: Construct an adjacency matrix based on the obtained historical vehicle speed information;

[0073] Step S3: constructing the LSTM-CNN neural network;

[0074] Step S4: using the adjacency matrix as an input to train the LSTM-CNN neural network, and using the gradient descent method to update and optimize the parameters of the LSTM-CNN neural network;

[0075] Step S5: Step S5 is performed iteratively until the LSTM-CNN neural network converges;

[0076] Step S6: Input the adjacency matrix of real-time vehicle speed information to the LSTM-CNN neural network to predict the road network traffic status....

Embodiment 2

[0122] Such as figure 1 , figure 2 , image 3 , Figure 4 As shown, in this embodiment, the step size of the prior data and the prediction result are both set to 5 minutes. In order to test the reliability of the prediction effect of the model under different prior conditions, this setting has prior information of four lengths: 15min, 30min, 45min and 60min, and the corresponding step size is H={3,6,9,12}, The prediction lengths are 5min, 15min and 30min, and the corresponding step lengths are P={1,3,6}.

[0123] This embodiment is built based on the Tensorflow deep learning framework. After screening, the model parameters are determined as: in the convolutional network, use 6 convolution kernels with a size of (3,3), and use an average pooling layer with a size of (2,2 ), the number of neurons in the fully connected layer is 400, in the long short-term memory neural network, a layer of LSTM network is used, the number of nodes is 256, the final output is through a layer ...

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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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01G06N3/04
CPCG06N3/04G08G1/0104G08G1/0141
Inventor 陈锐祥王家伟何兆成
Owner SUN YAT SEN UNIV
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