LSTM-CNN-based urban road network traffic state prediction method
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A technology of traffic status and prediction method, applied in the field of intelligent transportation, which can solve problems such as only considering the time dimension
Active Publication Date: 2019-03-29
SUN YAT SEN UNIV
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[0007] In order to solve the shortcomings of the prior art that cannot use the nonlinear relationship of traffic state evolution and only conside...
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Embodiment 1
[0069] Such as 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: the adjacency matrix is used as input to train the LSTM-CNN neural network, and utilize 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 traffic status of the ...
Embodiment 2
[0122] Such as figure 1 , figure 2 , image 3 , Figure 4 As shown, in this embodiment, the step size of prior data and prediction results is set to 5min. 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 follows: 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 of fully c...
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Abstract
The invention is oriented to the prediction of the traffic state of an urban road network, wherein the speed of each road section is set as a prediction object. According to the method, firstly, a road network adjacent matrix is filled with the speed of each road section, and then the traffic state of the road network during a certain period is represented. The spatial characteristics of the traffic state are learned through the convolutional neural network. After that, a long and short-term memory neural network is used for receiving spatial characteristics of different time periods, and thecharacteristic learning of the time dimension is introduced. Finally, the average travel speed of each road section of the road network is predicted by combining the two kinds of the characteristic information. Compared with a traditional statistics method, the LSTM-CNN based on the road network adjacent matrix is provided, and the space-time nonlinear relation of the road network traffic state can be mastered more compared with the existing deep learning method. The traffic state input based on the road network adjacent matrix is constructed, and the spatial characteristics of the road network traffic state are kept. The input of redundant information is reduced, and the passing is reduced. The traffic state characteristics of the road network are effectively learned, and a good prediction effect is achieved.
Description
technical field [0001] The present invention relates to the field of intelligent transportation, more specifically, relate to a kind of urban road network traffic state prediction method based on LSTM-CNN. 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 ...
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