Abstract
Traffic speed prediction is a crucial and fundamental task of the intelligent transportation systems (ITS). Due to the dynamic and non-linear nature of the traffic, this task is difficult. Nonetheless, the collection of crowd map queries data brings new ways to solve this problem. Generally speaking, in a short period of time, a large amount of crowd map queries aiming at the same destination may lead to traffic congestion. For instance, large queries for Family Restaurant during the dinner time lead to traffic jams around it. However, traffic speed prediction with crowd map queries is challenging due to the complexity and scale of the map queries, as well as their modalities. To bridge the gap, we propose Multi-Seq2Seq-Att for hotspot traffic speed prediction. Multi-Seq2Seq-Att is a multi-modal sequence learning model that deals with two sequences in different modalities, namely, the query sequence and the traffic speed sequence. The main idea of Multi-Seq2Seq-Att is to learn to fuse the multi-modal sequence with content attention. With this method, Multi-Seq2Seq-Att addresses the modality gap between queries and the traffic speed. Experiments on real-world datasets from Baidu Map demonstrates a 24% relative boost over other state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Ahmed, M.S., Cook, A.R.: Analysis of freeway traffic time-series data by using Box-Jenkins techniques. No. 722 (1979)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Davis, G.A., Nihan, N.L.: Nonparametric regression and short-term freeway traffic forecasting. J. Transp. Eng. 117(2), 178–188 (1991)
Gustafsson, F.: Determining the initial states in forward-backward filtering. IEEE Trans. Sig. Process. 44(4), 988–992 (1996)
He, J., Shen, W., Divakaruni, P., Wynter, L., Lawrence, R.: Improving traffic prediction with tweet semantics. In: IJCAI, pp. 1387–1393 (2013)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191–2201 (2014)
Jin, X., Zhang, Y., Yao, D.: Simultaneously prediction of network traffic flow based on PCA-SVR. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4492, pp. 1022–1031. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72393-6_121
Leshem, G., Ritov, Y.: Traffic flow prediction using adaboost algorithm with random forests as a weak learner. Proc. World Acad. Sci. Eng. Technol. 19, 193–198 (2007)
Liao, B., Zhang, J., Wu, C., McIlwraith, D., Chen, T., Yang, S., Guo, Y., Wu, F.: Deep sequence learning with auxiliary information for traffic prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2018)
Liu, X., Kong, X., Li, Y.: Collective traffic prediction with partially observed traffic history using location-based social media. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. pp. 2179–2184. ACM (2016)
Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1412–1421 (2015)
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)
Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C: Emerg. Technol. 54, 187–197 (2015)
Tran, Q.T., Ma, Z., Li, H., Hao, L., Trinh, Q.K.: A multiplicative seasonal arima/garch model in evn traffic prediction. Int. J. Commun. Network Syst. Sci. 8(04), 43 (2015)
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)
Zhang, J., Wang, F.Y., Wang, K., Lin, W.H., Xu, X., Chen, C.: Data-driven intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 12(4), 1624–1639 (2011)
Acknowledgments
This work was supported in part by 973 program (No. 2015CB352302, 2015CB352300), the National Natural Science Foundation of China (Nos. 61625107, 61751209, U1611461), the Key Program of Zhejiang Province, China (No. 2015C01027) and Chinese Knowledge Center of Engineering Science and Technology (CKCEST).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Liao, B., Tang, S., Yang, S., Zhu, W., Wu, F. (2018). Multi-modal Sequence to Sequence Learning with Content Attention for Hotspot Traffic Speed Prediction. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11164. Springer, Cham. https://doi.org/10.1007/978-3-030-00776-8_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-00776-8_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00775-1
Online ISBN: 978-3-030-00776-8
eBook Packages: Computer ScienceComputer Science (R0)