Abstract
Traffic congestion prediction is a fundamental yet challenging problem in Intelligent Transportation Systems (ITS). Due to the large scale of the road network, the high nonlinearity and complexity of traffic data, few methods are well-suited for citywide traffic congestion prediction. In this paper, we propose a novel deep model named AF-TCP for Traffic Congestion Prediction at Arbitrary road segment and Flexible future time. For the model to achieve the prediction at flexible future time, we construct all time representations in a unified vector space and further improve the model’s perception ability to different horizons. On the other hand, to realize the congestion prediction of arbitrary road segment within the city, we utilize road attributes and local neighbor structure to build the road segment representation, and design a deep model to fuse it with the corresponding historical traffic data. Extensive experiments on the real-world dataset demonstrate that our model exhibits stable performance at different prediction horizons and outperforms the baselines.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Basak, S., Dubey, A., Bruno, L.: Analyzing the cascading effect of traffic congestion using LSTM networks. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 2144–2153. IEEE (2019)
Li, K., Chen, L., Shang, S.: Towards alleviating traffic congestion: optimal route planning for massive-scale trips. In: IJCAI (2020)
Chen, C., Zhang, D., Ma, X., Guo, B., Wang, L., Wang, Y., Sha, E.: Crowddeliver: planning city-wide package delivery paths leveraging the crowd of taxis. IEEE Trans. Intell. Trans. Syst. 18(6), 1478–1496 (2016)
Wang, D., Zhang, J., Cao, W., Li, J., Zheng, Y.: When will you arrive? AAAI, Estimating travel time based on deep neural networks. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Chen, C., Ding, Y., Xie, X., Zhang, S., Wang, Z., Feng, L.: Trajcompressor: an online map-matching-based trajectory compression framework leveraging vehicle heading direction and change. IEEE Trans. Intell. Transp. Syst. 21(5), 2012–2028 (2019)
Alghamdi, T., Elgazzar, K., Bayoumi, M., Sharaf, T., Shah, S.: Forecasting traffic congestion using ARIMA modeling. In: 2019 15th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1227–1232 (2019)
Nguyen, H.N., Krishnakumari, P., Vu, H.L., van Lint, H.: Traffic congestion pattern classification using multi-class svm. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 1059–1064 (2016)
Harrou, F., Zeroual, A., Sun, Y.: Traffic congestion monitoring using an improved KNN strategy. Measurement 156, 107534 (2020)
Zhao, Z., Chen, W., Wu, X., Chen, P.C.Y., Liu, J.: LSTM network: a deep learning approach for short-term traffic forecast. Iet Intell. Trans. Syst. 11, 68–75 (2017)
Liao, C., Chen, C., Xiang, C., Huang, H., Xie, H., Guo, S.: Taxi-passenger’s destination prediction via gps embedding and attention-based bilstm model. IEEE Trans. Intell. Transp. Syst. pp. 1–14 (2021)
Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: AAAI (2017)
Chen, M., Yu, X., Liu, Y.: PCNN: Deep convolutional networks for short-term traffic congestion prediction. IEEE Trans. Intell. Transp. Syst. 19, 3550–3559 (2018)
Yu, T., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: IJCAI (2018)
Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: AAAI (2019)
Fang, X., Huang, J., Wang, F., Zeng, L., Liang, H., Wang, H.: ConSTGAT: contextual spatial-temporal graph attention network for travel time estimation at baidu maps. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2020)
Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., Li, H.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21, 3848–3858 (2020)
Hu, J., Guo, C., Yang, B., Jensen, C.S.: Stochastic weight completion for road networks using graph convolutional networks. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1274–1285 (2019)
Zhang, D., Yin, J., Zhu, X., Zhang, C.: Network representation learning: a survey. IEEE Trans. Big Data 6, 3–28 (2020)
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998–6008 (2017)
Vapnik, V.: The nature of statistical learning theory. Springer science and business media (2013)
Ke, G., et al.: Lightgbm: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, pp. 3146–3154 (2017)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Adv. Neural Inf. Process. Syst. 27, 3104–3112 (2014)
Chen, C., Zhang, D., Castro, P.S., Li, N., Sun, L., Li, S., Wang, Z.: iboat: isolation-based online anomalous trajectory detection. IEEE Trans. Intell. Transp. Syst. 14(2), 806–818 (2013)
Chen, C., Liu, Q., Wang, X., Liao, C., Zhang, D.: semi-traj2graph: identifying fine-grained driving style with gps trajectory data via multi-task learning. IEEE Trans. Big Data (2021)
Guo, J., Huang, W., Williams, B.M.: Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transp. Res. Part C-emerging Technol. 43, 50–64 (2014)
Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L.: Predicting taxi-passenger demand using streaming data. IEEE Trans. Intell. Transp. Syst. 14, 1393–1402 (2013)
Chen, C., Zhang, D., Wang, Y., Huang, H.: Enabling Smart Urban Services with GPS Trajectory Data. Springer (2021) https://doi.org/10.1007/978-981-16-0178-1
Ma, X., Tao, Z., Wang, Y., Yu, H.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C-Emerging Technol. 54, 187–197 (2015)
Ye, J., Zhao, J., Ye, K., Xu, C.: How to build a graph-based deep learning architecture in traffic domain: a survey. ArXiv abs/2005.11691 (2020)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: ICLR (2018)
Acknowledgments
The work was supported by the National Natural Science Foundation of China (No. 61872050 and No. 62172066), and sponsored by DiDi GAIA Research Collaboration Plan. Xuefeng Xie and Jie Zhao contributed equally to this work and share the first authorship.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Xie, X., Zhao, J., Chen, C., Wang, L. (2022). AF-TCP: Traffic Congestion Prediction at Arbitrary Road Segment and Flexible Future Time. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13157. Springer, Cham. https://doi.org/10.1007/978-3-030-95391-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-95391-1_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95390-4
Online ISBN: 978-3-030-95391-1
eBook Packages: Computer ScienceComputer Science (R0)