A Road-Aware Neural Network for Multi-step Vehicle Trajectory Prediction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)

Abstract

Multi-step vehicle trajectory prediction has been of great significance for location-based services, e.g., actionable advertising. Prior works focused on adopting pattern-matching techniques or HMM-based models, where the ability of accurate prediction is limited since patterns and features are mostly extracted from historical trajectories. However, these methods may become weak to multi-step trajectory prediction when new patterns appear or the previous trajectory is incomplete.

In this paper, we propose a neural network model combining road-aware features to solve multi-step vehicle trajectory prediction task. We introduce a novel way of extracting road-aware features for vehicle trajectory, which consist of intra-road feature and inter-road feature extracted from road networks. The utilization of road-aware features helps to draw the latent patterns more accurately and enhances the prediction performances. Then we leverage LSTM units to build temporal dependencies on previous trajectory path and generate future trajectory. We conducted extensive experiments on two real-world datasets and demonstrated that our model achieved higher prediction accuracy compared with competitive trajectory prediction methods.

Keywords

Multi-step trajectory prediction Road-aware features LSTM 

Notes

Acknowledgment

This research is supported in part by 973 Program (No. 2014CB340303), NSFC (No. 61772341, 61472254, 61170238, 61602297 and 61472241), and Singapore NRF (CREATE E2S2). This work is also supported by the Program for Changjiang Young Scholars in University of China, the Program for China Top Young Talents, and the Program for Shanghai Top Young Talents.

References

  1. 1.
    Hendawi, A.M., Bao, J., Mokbel, M.F., Ali, M.: Predictive tree: an efficient index for predictive queries on road networks. In: 2015 IEEE 31st International Conference on Data Engineering (ICDE), pp. 1215–1226. IEEE (2015)Google Scholar
  2. 2.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  3. 3.
    Jeung, H., Liu, Q., Shen, H.T., Zhou, X.: A hybrid prediction model for moving objects. In: IEEE 24th International Conference on Data Engineering, ICDE 2008, pp. 70–79. IEEE (2008)Google Scholar
  4. 4.
    Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S.: Path prediction and predictive range querying in road network databases. VLDB J. 19(4), 585–602 (2010)CrossRefGoogle Scholar
  5. 5.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  6. 6.
    Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: WhereNext: a location predictor on trajectory pattern mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 637–646. ACM (2009)Google Scholar
  7. 7.
    Morzy, M.: Prediction of moving object location based on frequent trajectories. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds.) ISCIS 2006. LNCS, vol. 4263, pp. 583–592. Springer, Heidelberg (2006).  https://doi.org/10.1007/11902140_62CrossRefGoogle Scholar
  8. 8.
    Morzy, M.: Mining frequent trajectories of moving objects for location prediction. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 667–680. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-73499-4_50CrossRefGoogle Scholar
  9. 9.
    Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)Google Scholar
  10. 10.
    Qiao, S., Han, N., Zhu, W., Gutierrez, L.A.: TraPlan: an effective three-in-one trajectory-prediction model in transportation networks. IEEE Trans. Intell. Transp. Syst. 16(3), 1188–1198 (2015)CrossRefGoogle Scholar
  11. 11.
    Qiao, S., Shen, D., Wang, X., Han, N., Zhu, W.: A self-adaptive parameter selection trajectory prediction approach via hidden Markov models. IEEE Trans. Intell. Transp. Syst. 16(1), 284–296 (2015)CrossRefGoogle Scholar
  12. 12.
    Raymond, R., Morimura, T., Osogami, T., Hirosue, N.: Map matching with hidden Markov model on sampled road network. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 2242–2245. IEEE (2012)Google Scholar
  13. 13.
    Tao, Y., Faloutsos, C., Papadias, D., Liu, B.: Prediction and indexing of moving objects with unknown motion patterns. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, pp. 611–622. ACM (2004)Google Scholar
  14. 14.
    Ying, J.J.C., Lee, W.C., Weng, T.C., Tseng, V.S.: Semantic trajectory mining for location prediction. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 34–43. ACM (2011)Google Scholar
  15. 15.
    Zhou, J., Tung, A.K., Wu, W., Ng, W.S.: A semi-lazy approach to probabilistic path prediction in dynamic environments. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 748–756. ACM (2013)Google Scholar
  16. 16.
    Zhou, X., Shen, Y., Zhu, Y., Huang, L.: Predicting multi-step citywide passenger demands using attention-based neural networks. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 736–744. ACM (2018)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jingze Cui
    • 1
  • Xian Zhou
    • 1
  • Yanmin Zhu
    • 1
  • Yanyan Shen
    • 1
  1. 1.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations