Abstract
This paper proposes the fine-grained traffic prediction task (e.g. interval between data points is 1 min), which is essential to traffic-related downstream applications. Under this setting, traffic flow is highly influenced by traffic signals and the correlation between traffic nodes is dynamic. As a result, the traffic data is non-smooth between nodes, and hard to utilize previous methods which focus on smooth traffic data. To address this problem, we propose Fine-grained Deep Traffic Inference, termed as FDTI. Specifically, we construct a fine-grained traffic graph based on traffic signals to model the inter-road relations. Then, a physically-interpretable dynamic mobility convolution module is proposed to capture vehicle moving dynamics controlled by the traffic signals. Furthermore, traffic flow conservation is introduced to accurately infer future volume. Extensive experiments demonstrate that our method achieves state-of-the-art performance and learned traffic dynamics with good properties. To the best of our knowledge, we are the first to conduct the city-level fine-grained traffic prediction.
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Akagi, Y., Nishimura, T., Kurashima, T., Toda, H.: A fast and accurate method for estimating people flow from spatiotemporal population data. In: IJCAI, pp. 3293–3300 (2018)
Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. arXiv preprint arXiv:2007.02842 (2020)
Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks. arXiv preprint arXiv:1506.03099 (2015)
Chen, D., Lin, Y., Li, W., Li, P., Zhou, J., Sun, X.: Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3438–3445 (2020)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
Chen, Y., Segovia, I., Gel, Y.R.: Z-GCNets: time zigzags at graph convolutional networks for time series forecasting. In: International Conference on Machine Learning, pp. 1684–1694. PMLR (2021)
Choi, J., Choi, H., Hwang, J., Park, N.: Graph neural controlled differential equations for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 6367–6374 (2022)
Cirstea, R.G., Yang, B., Guo, C., Kieu, T., Pan, S.: Towards spatio-temporal aware traffic time series forecasting. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 2900–2913. IEEE (2022)
Diao, Z., Wang, X., Zhang, D., Liu, Y., Xie, K., He, S.: Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 890–897 (2019)
Fang, S., Zhang, Q., Meng, G., Xiang, S., Pan, C.: GSTNet: global spatial-temporal network for traffic flow prediction. In: IJCAI, pp. 2286–2293 (2019)
Fang, Z., Long, Q., Song, G., Xie, K.: Spatial-temporal graph ode networks for traffic flow forecasting. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 364–373 (2021)
Fang, Z., Pan, L., Chen, L., Du, Y., Gao, Y.: MDTP: a multi-source deep traffic prediction framework over spatio-temporal trajectory data. Proc. VLDB Endow. 14(8), 1289–1297 (2021)
Guo, K., Hu, Y., Sun, Y., Qian, S., Gao, J., Yin, B.: Hierarchical graph convolution network for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 151–159 (2021)
Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 922–929 (2019)
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017)
Han, L., Du, B., Sun, L., Fu, Y., Lv, Y., Xiong, H.: Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 547–555 (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Huang, R., Huang, C., Liu, Y., Dai, G., Kong, W.: LSGCN: long short-term traffic prediction with graph convolutional networks. In: IJCAI, pp. 2355–2361 (2020)
Ji, J., Wang, J., Jiang, Z., Jiang, J., Zhang, H.: STDEN: towards physics-guided neural networks for traffic flow prediction (2022)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Koonce, P., Rodegerdts, L.: Traffic signal timing manual. Technical report, United States. Federal Highway Administration (2008)
Lan, S., Ma, Y., Huang, W., Wang, W., Yang, H., Li, P.: DSTAGNN: dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In: International Conference on Machine Learning, pp. 11906–11917. PMLR (2022)
Lee, H., Jin, S., Chu, H., Lim, H., Ko, S.: Learning to remember patterns: pattern matching memory networks for traffic forecasting. arXiv preprint arXiv:2110.10380 (2021)
Lei, X., Mei, H., Shi, B., Wei, H.: Modeling network-level traffic flow transitions on sparse data. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 835–845 (2022)
Li, F., et al.: Dynamic graph convolutional recurrent network for traffic prediction: benchmark and solution. ACM Trans. Knowl. Discov. Data (TKDD) (2021)
Li, M., Zhu, Z.: Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4189–4196 (2021)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)
Liang, C., et al.: CBLAB: scalable traffic simulation with enriched data supporting. arXiv preprint arXiv:2210.00896 (2022)
Lippi, M., Bertini, M., Frasconi, P.: Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning. IEEE Trans. Intell. Transp. Syst. 14(2), 871–882 (2013)
Nikravesh, A.Y., Ajila, S.A., Lung, C.H., Ding, W.: Mobile network traffic prediction using MLP, MLPWD, and SVM. In: 2016 IEEE International Congress on Big Data (BigData Congress), pp. 402–409. IEEE (2016)
Okutani, I., Stephanedes, Y.J.: Dynamic prediction of traffic volume through Kalman filtering theory. Transport. Res. Part B: Methodol. 18(1), 1–11 (1984)
Oreshkin, B.N., Amini, A., Coyle, L., Coates, M.: FC-GAGA: fully connected gated graph architecture for spatio-temporal traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 9233–9241 (2021)
Ouyang, K., et al.: Fine-grained urban flow inference. IEEE Trans. Knowl. Data Eng. 34(6), 2755–2770 (2020)
Qu, H., Gong, Y., Chen, M., Zhang, J., Zheng, Y., Yin, Y.: Forecasting fine-grained urban flows via spatio-temporal contrastive self-supervision. IEEE Trans. Knowl. Data Eng. (2022)
Rao, X., Wang, H., Zhang, L., Li, J., Shang, S., Han, P.: Fogs: first-order gradient supervision with learning-based graph for traffic flow forecasting. In: Proceedings of International Joint Conference on Artificial Intelligence, IJCAI. ijcai. org (2022)
Shao, Z., et al.: Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. arXiv preprint arXiv:2206.09112 (2022)
Song, C., Lin, Y., Guo, S., Wan, H.: Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 914–921 (2020)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Wei, H., Zheng, G., Gayah, V., Li, Z.: A survey on traffic signal control methods. arXiv preprint arXiv:1904.08117 (2019)
Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal Arima process: theoretical basis and empirical results. J. Transp. Eng. 129(6), 664–672 (2003)
Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 753–763 (2020)
Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)
Xie, P., Li, T., Liu, J., Du, S., Yang, X., Zhang, J.: Urban flow prediction from spatiotemporal data using machine learning: A survey. Inf. Fusion 59, 1–12 (2020)
Yao, H., Tang, X., Wei, H., Zheng, G., Li, Z.: Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5668–5675 (2019)
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)
Zheng, C., Fan, X., Wang, C., Qi, J.: GMAN: a graph multi-attention network for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 1234–1241 (2020)
Zheng, G., et al.: Learning phase competition for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1963–1972 (2019)
Zhu, Z., Peng, B., Xiong, C., Zhang, L.: Short-term traffic flow prediction with linear conditional gaussian Bayesian network. J. Adv. Transp. 50(6), 1111–1123 (2016)
Acknowledgement
This work was sponsored by National Key Research and Development Program of China under Grant No.2022YFB3904204, National Natural Science Foundation of China under Grant No. 62102246, 62272301, and Provincial Key Research and Development Program of Zhejiang under Grant No. 2021C01034.
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Ethical Statement
The data used in this paper is collected from the wildly-used traffic simulator of KDDCUP2021 and does not contain any personal or sensitive data. The authors ensured that the data was collected in an ethical and legal manner. Hence, no personally identifiable information was obtained and people can not infer personal information through the data. The potential use of this work is accurate traffic prediction and better support of downstream tasks such as traffic signal control. This work is not potentially a part of policing or military work. The authors of this paper are committed to ethical principles and guidelines in conducting research and have taken measures to ensure the integrity and validity of the data. The use of the data in this study is in accordance with ethical standards and is intended to advance knowledge in the field of traffic prediction.
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Liu, Z., Liang, C., Zheng, G., Wei, H. (2023). FDTI: Fine-Grained Deep Traffic Inference with Roadnet-Enriched Graph. In: De Francisci Morales, G., Perlich, C., Ruchansky, N., Kourtellis, N., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14175. Springer, Cham. https://doi.org/10.1007/978-3-031-43430-3_11
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