Academic Journal of Computing & Information Science, 2020, 3(1); doi: 10.25236/AJCIS.2020.030106.
Ming Shi, Shaowei Cai, Qingming Yi
School of Information Science and Technology, Jinan University, Guangzhou 510632, China.
It is very important to use the convolutional neural network model for urban short-term traffic congestion forecasting. However, pooling operation process is prone to cause data structure information loss when using CNN model to predict short-term traffic flow. Complete data feature information cannot be transmitted, which reduces the prediction ability of the model. To solve these problems, an innovative model based on dilated convolutional-dense networks is proposed in this paper. Firstly, the model can use the dilated convolution to obtain the characteristics of the larger receptive field with fewer network parameters, and fully extract the complex and variable data features. Then, through up-sampling and densely connected, the problem of parameter degradation in the process of increasing layers of neural network can be solved. Finally, the actual urban road average speed data blocks are taken to verify the validity of the model. Experimental results show that compared with traditional CNN model, the average absolute error of the network prediction structure is reduced by 3% to 23%.
convolutional-dense network; spatio-temporal characteristics; convolutional neural networks; short-term traffic congestion prediction
Ming Shi, Shaowei Cai, Qingming Yi. Dilated-Dense Network for traffic congestion prediction Model. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 1: 46-58. https://doi.org/10.25236/AJCIS.2020.030106.
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