收稿日期: 2019-10-29
网络出版日期: 2021-03-03
基金资助
国家青年科学基金(61603153);广州市产业技术重大攻关技术项目(201802010028);广州市“羊城创新创业领军人才支持计划”(2019019)
A Traffic Congestion Prediction Model Based on Dilated-Dense Network
Received date: 2019-10-29
Online published: 2021-03-03
在利用卷积神经网络模型对短时交通拥堵情况等预测场景进行预测时，由于模型的卷积池化操作过程会丢失部分数据，使得目标位置的信息出现丢失及特征的分辨率持续下降，导致模型的预测能力降低．针对此，本文提出一种空洞-稠密神经网络模型．首先，利用空洞卷积用较少的网络参数获取更大感受野的特点，充分提取出复杂多变的数据时空特征．其次，通过下采样及稠密网络的等值映射，解决参数在神经网络层数增加过程出现退化的问题．最后，取实际的城市道路平均车速数据块对网络结构的有效性进行验证．结果表明：同卷积神经网络模型相比，该网络结构预测平均绝对误差降低3%～23%.
石敏, 蔡少委, 易清明 . 基于空洞-稠密网络的交通拥堵预测模型[J]. 上海交通大学学报, 2021 , 55(2) : 124 -130 . DOI: 10.16183/j.cnki.jsjtu.2020.99.009
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%.
| [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] | 陈琛，吴青，高嵩．基于时空因素的水路短期货运量预测[J]. 上海交通大学学报，2019, 53 (5): 556-562. |
| [3] | CHEN Chen, WU Qing, GAO Song. Short-term shipping freight volume prediction based on temporal-spatial features [J]. Journal of Shanghai Jiao Tong 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] | 罗文慧，董宝田，王泽胜．基于CNN-SVR混合深度学习模型的短时交通流预测[J]. 交通运输系统工程与信息，2017, 17(5): 68-74. |
| [5] | LUO Wenhui, DONG Baotian, WANG Zesheng. Short-term traffic flow prediction based on CNN-SVR hybrid deep learning model [J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(5): 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(3): 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, 29(1): 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). Piscataway, NJ, USA: 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, 21(1): 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 tra-fficNet [C]∥2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA). Piscataway, NJ, USA: IEEE，2018: 1134-1139. |
| [11] | 张伟斌，余英豪，戚湧，等. 基于时空分析和CNN的交通流量短时预测方法[C]∥第十三届中国智能交通年会大会．北京： 电子工业出版社，2018: 349-361. |
| [11] | ZHANG Weibin, YU Yinghao, QI Yong, et al. Short-term traffic flow prediction based on spatio temporal analysis and CNN deep learning[C]∥The 13th China Intelligent Transportation Annual Meeting. Beijing: Publishing House of Electronics Industry, 2018: 349-361. |
| [12] | LIU Q, WANG B, ZHU Y, et al. 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. |
| [13] | YU F, KOLTUN V. Multi-scale context aggregation by dilated convolutions[C]∥International Conference on Learning Representations (ICLR). Montevideo, Uruguay: IEEE, 2016: 1-13. |
| [14] | HUANG G, LIU Z, VAN DER, et al. Densely connected convolutional networks[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ, USA: IEEE, 2017: 2261-2269. |
| [15] | 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, 38(5): 1-13. |
| [16] | 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. |
| [17] | KINGMA D K, BA J L. Adam: A method for stochastic optimization[C]∥International Conference for Learning Representations (ICLR). Montevideo, Uruguay: IEEE, 2018: 6980-6995. |
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