Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction
Source abstract
Abstract
Traffic speed prediction plays a significant role in the intelligent traffic system (ITS). However, due to the complex spatial-temporal correlations of traffic data, it is very challenging to predict traffic speed timely and accurately. The traffic speed renders not only short-term neighboring and multiple long-term periodic dependencies in the temporal dimension but also local and global dependencies in the spatial dimension. To address this problem, we propose a novel deep-learning-based model, Global Spatial-Temporal Graph Convolutional Network (GSTGCN), for urban traffic speed prediction. The model consists of three spatial-temporal components with the same structure and an external component. The three spatial-temporal components are used to model the recent, daily-periodic, and weekly-periodic spatial-temporal correlations of the traffic data, respectively. More specifically, each spatial-temporal component consists of a dynamic temporal module and a global correlated spatial module. The former contains multiple residual blocks which are stacked by dilated casual convolutions, while the latter contains a localized graph convolution and a global correlated mechanism. The external component is used to extract the effect of external factors, such as holidays and weather conditions, on the traffic speed. Experimental results on two real-world traffic datasets have demonstrated that the proposed GSTGCN outperforms the state-of-the-art baselines.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic Prediction and Management Techniques · Transportation Planning and Optimization · Human Mobility and Location-Based Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science, Chongqing University, Chongqing 400044, China
Chongqing Key Laboratory of Software Theory & Technology, Chongqing 400044, China
References (47)
Show source-provided references
1. Artuñedo, A., Del Toro, R.M., and Haber, R.E. (2017). Consensus-based cooperative control based on pollution sensing and traffic information for urban traffic networks. Sensors, 17.10.3390/s17050953 ↗
2. Castaño, F., Strzelczak, S., Villalonga, A., Haber, R.E., and Kossakowska, J. (2019). Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study. Remote Sens., 11.10.3390/rs11192252 ↗
3. Castaño, F., Beruvides, G., Villalonga, A., and Haber, R.E. (2018). Self-tuning method for increased obstacle detection reliability based on internet of things LiDAR sensor models. Sensors, 18.10.20944/preprints201802.0192.v1 ↗
4. ARMA models and the Box–Jenkins methodology10.1002/(SICI)1099-131X(199705)16:3<147::AID-FOR652>3.0.CO;2-X ↗
5. Zivot, E., and Wang, J. (2006). Vector autoregressive models for multivariate time series. Modeling Financial Time Series with S-Plus®, Springer.
6. Short-term traffic volume forecasting: A k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm10.1016/j.trc.2014.02.009 ↗
7. Travel-time prediction with support vector regression10.1109/TITS.2004.837813 ↗
8. Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning10.1109/TITS.2013.2247040 ↗
9. Du, S., Li, T., Gong, X., Yu, Z., Huang, Y., and Horng, S.J. (2018). A hybrid method for traffic flow forecasting using multimodal deep learning. arXiv.
10. Yao, H., Tang, X., Wei, H., Zheng, G., Yu, Y., and Li, Z. (2018). Modeling spatial-temporal dynamics for traffic prediction. arXiv.
11. Yao, H., Wu, F., Ke, J., Tang, X., Jia, Y., Lu, S., Gong, P., Ye, J., and Li, Z. (2018, January 2–7). Deep multi-view spatial-temporal network for taxi demand prediction. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.10.1609/aaai.v32i1.11836 ↗
12. Li, Y., Yu, R., Shahabi, C., and Liu, Y. (May, January 30). Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. Proceedings of the International Conference on Learning Representations (ICLR ’18), Vancouver, BC, Canada.
13. Yu, B., Yin, H., and Zhu, Z. (2018, January 13–19). Spatio-temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden.10.24963/ijcai.2018/505 ↗
14. Wu, Z., Pan, S., Long, G., Jiang, J., and Zhang, C. (2019). Graph WaveNet for Deep Spatial-Temporal Graph Modeling. arXiv.10.24963/ijcai.2019/264 ↗
15. Oord, A.V.D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K. (2016). Wavenet: A generative model for raw audio. arXiv.
16. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
17. Chen, J., Li, D., Zhang, G., and Zhang, X. (2018). Localized space-time autoregressive parameters estimation for traffic flow prediction in urban road networks. Appl. Sci., 8.10.3390/app8020277 ↗
18. Chen, W., Chen, L., Xie, Y., Cao, W., Gao, Y., and Feng, X. (2019). Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting. arXiv.10.1609/aaai.v34i04.5758 ↗
19. Yao, H., Tang, X., Wei, H., Zheng, G., and Li, Z. (February, January 27). Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. Proceedings of the AAAI Conference on Artificial Intelligence, Hawaii, HI, USA.
20. Traffic flow prediction with big data: A deep learning approach
21. Spatiotemporal traffic flow prediction with KNN and LSTM10.1155/2019/4145353 ↗
22. Long short-term memory10.1162/neco.1997.9.8.1735 ↗
23. Yu, R., Li, Y., Shahabi, C., Demiryurek, U., and Liu, Y. (2017, January 27–29). Deep learning: A generic approach for extreme condition traffic forecasting. Proceedings of the 2017 SIAM International Conference on Data Mining, Houston, TX, USA.10.1137/1.9781611974973.87 ↗
24. Cui, Z., Ke, R., and Wang, Y. (2017, January 14). Deep stacked bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. Proceedings of the 6th International Workshop on Urban Computing (UrbComp 2017), Halifax, NS, Canada.
25. Zhang, J., Zheng, Y., and Qi, D. (2017, January 4–9). Deep spatio-temporal residual networks for citywide crowd flows prediction. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.10.1609/aaai.v31i1.10735 ↗
26. Liu, P., Zhang, Y., Kong, D., and Yin, B. (2019). Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction. Appl. Sci., 9.10.3390/app9040615 ↗
27. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P.S. (2019). A comprehensive survey on graph neural networks. arXiv.
28. Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y. (2013). Spectral networks and locally connected networks on graphs. arXiv.
29. Defferrard, M., Bresson, X., and Vandergheynst, P. (2016). Convolutional neural networks on graphs with fast localized spectral filtering. Advances in Neural Information Processing Systems, Neural Information Processing Systems (NIPS).
30. Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv.
31. Atwood, J., and Towsley, D. (2016). Diffusion-convolutional neural networks. Advances in Neural Information Processing Systems, Neural Information Processing Systems (NIPS).
32. Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., and Dahl, G.E. (2017, January 6–11). Neural message passing for quantum chemistry. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia.
33. Hamilton, W., Ying, Z., and Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, Neural Information Processing Systems (NIPS).
34. Grover, A., and Leskovec, J. (2016, January 13–17). node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.10.1145/2939672.2939754 ↗
35. A survey on network embedding10.1109/TKDE.2018.2849727 ↗
36. Fang, S., Zhang, Q., Meng, G., Xiang, S., and Pan, C. (2019, January 10–16). Gstnet: Global spatial-temporal network for traffic flow prediction. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Macao, China.10.24963/ijcai.2019/317 ↗
37. Pan, Z., Liang, Y., Wang, W., Yu, Y., Zheng, Y., and Zhang, J. (2019, January 4–8). Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.10.1145/3292500.3330884 ↗
38. LeCun, Y.A., Bottou, L., Orr, G.B., and Müller, K.R. (2012). Efficient backprop. Neural Networks: Tricks of the Trade, Springer.10.1007/978-3-642-35289-8_3 ↗
39. (2018, November 20). OpenStreetMap. Available online: https://www.openstreetmap.org/.
40. (2018, October 25). Foursquare. Available online: https://developer.foursquare.com/.
41. (2020, January 13). Wikipedia. Available online: https://en.wikipedia.org/wiki/Cosine_similarity.
42. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains10.1109/MSP.2012.2235192 ↗
43. Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv.
44. Salimans, T., and Kingma, D.P. (2016). Weight normalization: A simple reparameterization to accelerate training of deep neural networks. Advances in Neural Information Processing Systems, Neural Information Processing Systems (NIPS).
45. Nair, V., and Hinton, G.E. (2010, January 21–24). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel.
46. (2019, September 25). Dark Sky. Available online: https://darksky.net/dev.
47. Freeway performance measurement system: Mining loop detector data10.3141/1748-12 ↗