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http://dx.doi.org/10.3837/tiis.2020.09.002

Traffic Flow Prediction Model Based on Spatio-Temporal Dilated Graph Convolution  

Sun, Xiufang (College of Computer Science and Technology, Qingdao University)
Li, Jianbo (College of Computer Science and Technology, Qingdao University)
Lv, Zhiqiang (College of Computer Science and Technology, Qingdao University)
Dong, Chuanhao (College of Computer Science and Technology, Qingdao University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.9, 2020 , pp. 3598-3614 More about this Journal
Abstract
With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.
Keywords
Traffic Flow Prediction; Deep Learning; Graph Convolution; Dilated Convolution;
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1 Baowei Wang, Weiwen Kong, Hui Guan, Neal N. Xiong, "Air Quality Forecasting Based on Gated Recurrent Long Short Term Memory Model in Internet of Things," IEEE Access, 7, 69524-69534, 2019.   DOI
2 Baowei Wang, Weiwen Kong, Naixue Xiong, "A dual-chaining watermark scheme for data integrity protection in Internet of Things," Cmc-computers Materials & Continua, 58(3), 679-695, 2019.   DOI
3 Belghachi Mohammed and Debab Naouel, "An Efficient Greedy Traffic Aware Routing Scheme for Internet of Vehicles," CMC-COMPUTERS MATERIALS & CONTINUA, 60(3), 959-972, 2019.   DOI
4 Huey-Kuo Chen, and Che-Jung Wu, "Travel time prediction using empirical mode decomposition and gray theory: Example of National Central University bus in Taiwan," Transportation research record, vol. 2324(1), pp.11-19, 2012.   DOI
5 Billy M Williams, and A.Hoel Lester, "Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results," Journal of transportation engineering, vol. 129, no. 6, pp. 664-672, 2003.   DOI
6 Xia, Zhuoqun, Zhenzhen Hu, and Junpeng Luo, "UPTP Vehicle Trajectory Prediction Based on User Preference Under Complexity Environment," Wireless Personal Communications, vol. 97, pp. 4651-4665, 2017.   DOI
7 Hongyu Sun, Henry Liu, Heng Xiao, Rachel He, Ran Bin, "Use of local linear regression model for short-term traffic forecasting," Transportation Research Record, vol. 1836, pp. 143-150, 2003.   DOI
8 Min Wanli, and Laura Wynter, "Real-time road traffic prediction with spatio-temporal correlations," Transportation Research Part C: Emerging Technologies, vol. 19, no. 4, pp. 606-616, 2011.   DOI
9 Vlahogianni, Eleni I., Matthew G. Karlaftis, and John C. Golias, "Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach," Transportation Research Part C: Emerging Technologies, vol. 13, no. 3, pp.211-234, 2005.   DOI
10 Vlahogianni, Eleni I, "Computational intelligence and optimization for transportation big data: challenges and opportunities," Engineering and Applied Sciences Optimization, Springer, Cham, pp. 107-128, 2015.
11 EDES, YORGOS J. STEPHAN, Panos G. Michalopoulos, and Roger A. Plum, "Improved estimation of traffic flow for real-time control," Transportation Research Record, vol. 95, pp. 28-39, 1980.
12 Ahmed, Mohammed S., and Allen R. Cook, "Analysis of freeway traffic time-series data by using Box-Jenkins techniques," No. 722, pp. 1-9, 1979.
13 Okutani, Iwao, and Yorgos J. Stephanedes, "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B: Methodological, vol. 18, no. 1, pp. 1-11, 1984.   DOI
14 Huifeng Ji, Aigong Xu, Xin Sui, Lanyong Li, "The applied research of Kalman in the dynamic travel time prediction," in Proc. of 2010 18th International Conference on Geoinformatics. IEEE, pp.1-5, 2010.
15 Defferrard, Michaël, Xavier Bresson, and Pierre Vandergheynst, "Convolutional neural networks on graphs with fast localized spectral filtering," in Proc. of NIPS'16: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 3844-3852, 2016.
16 Yisheng Lv, Yanjie Duan, Wenwen Kang, Zhengxi Li, "Traffic flow prediction with big data: a deep learning approach," IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 865-873, 2015.   DOI
17 Huang, Wenhao, Wenhao Huang, Guojie Song, Haikun Hong, Kunqing Xie, "Deep architecture for traffic flow prediction: deep belief networks with multitask learning," IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 5, pp. 2191-2201, 2014.   DOI
18 Quanjun Chen, Xuan Song, Harutoshi Yamada, Ryosuke Shibasaki, "Learning deep representation from big and heterogeneous data for traffic accident inference," in Proc. of Thirtieth AAAI Conference on Artificial Intelligence, pp. 338-344, 2016.
19 Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh, "A fast learning algorithm for deep belief nets," Neural computation, vol. 18, no. 7, pp. 1527-1554, 2006.   DOI
20 Jia, Yuhan, Jianping Wu and Yiman Du, "Traffic speed prediction using deep learning method," in Proc. of 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). IEEE, pp. 1217-1222, 2016.
21 Xingjian Shi, Zhourong Chen,Hao Wang,Dit-Yan Yeung,Wai Kin Wong, Wang-chun WOO, "Convolutional LSTM network: A machine learning approach for precipitation nowcasting," in Proc of NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, pp. 802-810, 2015.
22 Sepp Hochreiter, and Jürgen Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.   DOI
23 Yu, Bing, Haoteng Yin, and Zhanxing Zhu, "Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting," in Proc. of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), 3634-3640, 2017.
24 Kipf, Thomas N., and Max Welling, "Semi-supervised classification with graph convolutional networks," arXiv preprint arXiv:1609.02907, 2016.
25 Jiao Yao, Kaimin Zhang, Yaxuan Dai, Jin Wang, "Power Function-based Signal Recovery Transition Optimization Model of Emergency Traffic," Journal of Supercomputing, vol. 74, pp.7003-7023, 2018.   DOI
26 Ryder Benjamin, Dahlinger Andre, Gahr Bernhard, Zundritsch Peter, Wortmann Felix.and Fleisch Elgar, "Spatial prediction of traffic accidents with critical driving events-Insights from a nationwide field study," Transportation research part A: policy and practice, vol. 124, pp. 611-626, 2019.   DOI
27 Shuren Zhou, Wenlong Liang, Junguo Li, Jeong-Uk Kim, "Improved VGG Model for Road Traffic Sign Recognition," CMC-Computers, Materials & Continua, vol. 57, no. 1, pp.11-24, 2018.   DOI
28 Jianming Zhang, Wei Wang, Chaoquan Lu, Jin Wang, Arun Kumar Sangaiah, "Lightweight deep network for traffic sign classification," Annals of Telecommunications, vol. 75, pp. 369-379, 2020.   DOI
29 Yu, Fisher, and Vladlen Koltun, "Multi-scale context aggregation by dilated convolutions," arXiv preprint arXiv:1511.07122, 2015.