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Academic Journal of Computing & Information Science, 2020, 3(1); doi: 10.25236/AJCIS.2020.030106.

Dilated-Dense Network for traffic congestion prediction Model

Author(s)

Ming Shi, Shaowei Cai, Qingming Yi

Corresponding Author:
Ming Shi
Affiliation(s)

School of Information Science and Technology, Jinan University, Guangzhou 510632, China.

Abstract

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%.

Keywords

convolutional-dense network; spatio-temporal characteristics; convolutional neural networks; short-term traffic congestion prediction

Cite This Paper

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.

References

[1] Chen P, Ding C, Lu G, et al. Short-Term Traffic States Forecasting Considering Spatial–Temporal Impact on an Urban Expressway [J]. Transportation Research Record, 2016, 2594 (1): 61-72.
[2] Zhang W, Yu Y, Qi Y. Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning [J]. Transportation Research Record. 2019, 15 (2):1688-1711.
[3] Chen C, Wu Q, Gao S. Short-Term Shipping Freight Volume Prediction Based on Temporal-Spatial Features [J]. Journal of Shanghai Jiaotong University, 2019, 53 (5):556-562.
[4] Feng X, Ling X, Zheng H, et al. Adaptive Multi-Kernel SVM With Spatial–Temporal Correlation for Short-Term Traffic Flow Prediction [J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(6): 2001-2013.
[5] Luo W, Dong B, Wang Z. Short-term Traffic Flow Prediction Based on CNN-SVR Hybrid Deep Learning Model [J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(05):68-74.
[6] Deng S, Jia S, Chen J. Exploring spatial–temporal relations via deep convolutional neural networks for traffic flow prediction with incomplete data [J]. Applied Soft Computing, 2019, 78: 712-721.
[7] Lin F, Xu Y, Yang Y, et al. A Spatial-Temporal Hybrid Model for Short-Term Traffic Prediction [J]. Mathematical Problems in Engineering, 2019, 2019: 1-12.
[8] Kang D, Lv Y, Chen Y. Short-term traffic flow prediction with LSTM recurrent neural network [C]. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017: 1-6.
[9] An J, Fu L, Hu M, et al. A Novel Fuzzy-Based Convolutional Neural Network Method to Traffic Flow Prediction with Uncertain Traffic Accident Information [J]. IEEE Access, 2019, 7: 20708-20722.
[10] Wang P, Li L, Jin Y, et al. Detection of unwanted traffic congestion based on existing surveillance system using in freeway via a CNN-architecture trafficNet [C]// 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2018: 1134-1139.
[11] Liu Q, Wang B, Zhu Y. Short-Term Traffic Speed Forecasting Based on Attention Convolutional Neural Network for Arterials [J]. Computer-Aided Civil and Infrastructure Engineering, 2018, 33 (11): 999-1016.
[12] Yu F, Koltun V. Multi-Scale Context Aggregation by Dilated Convolutions [C]. International Conference on Learning Representations (ICLR). 2016,1-13.
[13] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks [C]. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700-4708.
[14] Dolz J, Gopinath K, Yuan J, et al. HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation [J]. IEEE transactions on medical imaging, 2018.
[15] Zhang C, Zhang H, Yuan D, et al. Citywide cellular traffic prediction based on densely connected convolutional neural networks [J]. IEEE Communications Letters, 2018, 22(8): 1656-1659.
[16] Kingma D K, Ba J L, Adam: A Method for Stochastic Optimization [C]. the 3rd International Conference for Learning Representations.2018, 6980—698.