A Traffic Congestion Prediction Model Based on Dilated-Dense Network

doi:10.16183/j.cnki.jsjtu.2020.99.009

Abstract

Abstract:

When using the convolutional neural network (CNN) model to predict short-term traffic congestion, due to the convolution pooling operation of the model, part of the data for the information of the target position will be lost, resulting in the decline of the resolution of the output features and the decrease in the predictive ability of the model. To solve this problem, this paper proposes a dilated-dense neural network model. First, it uses dilated convolution to obtain the characteristics of a larger receptive field with fewer network parameters, and fully extracts complex and variable data spatio-temporal characteristics. Then, through down-sampling and equivalent mapping of dense network, it solves the problem of parameter degradation in the process of increasing layers of neural network. Finally, it uses the actual urban road average speed data blocks to verify the validity of the model. The results show that compared with the convolutional neural network model, the average absolute error of the network structure prediction is reduced by 3% to 23%.

Key words: dilated-dense network, spatio-temporal characteristics, convolutional neural networks, short-term traffic congestion prediction

CLC Number:

TP391
U495

SHI Min, CAI Shaowei, YI Qingming. A Traffic Congestion Prediction Model Based on Dilated-Dense Network[J]. Journal of Shanghai Jiao Tong University, 2021, 55(2): 124-130.

Figures/Tables 7

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道路数	Loss
	采样间隔
	1、2、3	1、2、4	1、2、5
28	0.002 3	－	－
32	0.002 6	0.002 6	－
36	0.002 9	0.003 1	0.002 7
38	0.003 4	0.003 2	0.003 0
40	0.003 7	0.003 3	0.003 3
42	0.003 9	0.003 6	0.003 5

模型	MAE			RMSE
模型	假日	正常日	测试集	假日	正常日	测试集
LeNet	3.16	3.29	3.17	4.93	5.01	4.67
空洞卷积	2.61	2.65	2.72	3.73	3.79	3.86
空洞-残差结构	2.44	2.46	2.51	3.41	3.52	3.50
空洞-稠密结构	2.35	2.43	2.44	3.32	3.49	3.43