Predicting Passenger’s Public Transportation Travel Route Using Smart Card Data

  • Chen Yang
  • Wei Chen
  • Bolong Zheng
  • Tieke He
  • Kai Zheng
  • Han SuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10988)


Transit prediction is a important task for public transport institutions and urban planners to provide better transit scheduling and urban planning. In recent years, there are a lot of research on traffic prediction, but the existing works focus predicting the monolithic traffic trend, and few works focus on passenger’s public transportation travel route. In this paper, we study the passenger’s travel route and duration prediction. We propose a prediction model based on LSTM neural network to predict passenger’s travel route and duration. Specifically, we leverage multimodal embedding to extract passenger’s features which are highly related to passenger’s travel route and then use a LSTM-based model to improve the prediction accuracy. To verify the effectiveness of our model, we conduct extensive experiments using a real dataset which is collected from Brisbane in Australia for four months. The experimental results show that the accuracy of our model is better than baseline models.


Transit prediction Multimodal embedding Smart card 


  1. 1.
    Cho, K., van Merrienboer, B., Gülçehre, Ç., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, Doha, Qatar, 25–29 October 2014, A Meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1724–1734 (2014)Google Scholar
  2. 2.
    Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proc. Nat. Acad. Sci. U.S.A. 106(36), 15274–15278 (2009)CrossRefGoogle Scholar
  3. 3.
    Gao, L., Guo, Z., Zhang, H., Xu, X., Shen, H.T.: Video captioning with attention-based LSTM and semantic consistency. IEEE Trans. Multimed. 19(9), 2045–2055 (2017)CrossRefGoogle Scholar
  4. 4.
    Graves, A.: Generating sequences with recurrent neural networks. CoRR abs/1308.0850 (2013)Google Scholar
  5. 5.
    Gutmann, M., Hyvärinen, A.: Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. J. Mach. Learn. Res. 13, 307–361 (2012)MathSciNetzbMATHGoogle Scholar
  6. 6.
    He, L., Trépanier, M.: Estimating the destination of unlinked trips in transit smart card fare data. Transp. Res. Rec. J. Transp. Res. Board 2535(2535), 97–104 (2015)CrossRefGoogle Scholar
  7. 7.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  8. 8.
    Jiang, S., Ferreira Jr. J., González, M.C.: Discovering urban spatial-temporal structure from human activity patterns. In: Proceedings of the ACM SIGKDD International Workshop on Urban Computing, UrbComp@KDD 2012, Beijing, China, 12 August 2012, pp. 95–102 (2012)Google Scholar
  9. 9.
    Ma, X., Liu, C., Wen, H., Wang, Y., Wu, Y.J.: Understanding commuting patterns using transit smart card data. J. Transp. Geogr. 58, 135–145 (2017)CrossRefGoogle Scholar
  10. 10.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013, Proceedings of a Meeting held 5–8 December 2013, Lake Tahoe, Nevada, United States, pp. 3111–3119 (2013)Google Scholar
  11. 11.
    Paulos, E., Goodman, E.: The familiar stranger: anxiety, comfort, and play in public places. In: Proceedings of the 2004 Conference on Human Factors in Computing Systems, CHI 2004, Vienna, Austria, 24–29 April 2004, pp. 223–230 (2004)Google Scholar
  12. 12.
    Pearlmutter, B.A.: Gradient calculations for dynamic recurrent neural networks: a survey. IEEE Trans. Neural Netw. 6(5), 1212–1228 (1995)CrossRefGoogle Scholar
  13. 13.
    Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, Montreal, Quebec, Canada, 8–13 December 2014, pp. 3104–3112 (2014)Google Scholar
  14. 14.
    Wang, L., Yu, Z., Guo, B., Ku, T., Yi, F.: Moving destination prediction using sparse dataset: a mobility gradient descent approach. TKDD 11(3), 37:1–37:33 (2017)Google Scholar
  15. 15.
    Xiao, X., Zheng, Y., Luo, Q., Xie, X.: Inferring social ties between users with human location history. J. Ambient Intell. Humaniz. Comput. 5(1), 3–19 (2014)CrossRefGoogle Scholar
  16. 16.
    Yu, X., Pan, A., Tang, L.A., Li, Z., Han, J.: Geo-friends recommendation in GPS-based cyber-physical social network. In: International Conference on Advances in Social Networks Analysis and Mining, pp. 361–368 (2011)Google Scholar
  17. 17.
    Zhang, C., Zhang, K., Yuan, Q., Peng, H., Zheng, Y., Hanratty, T., Wang, S., Han, J.: Regions, periods, activities: uncovering urban dynamics via cross-modal representation learning. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, 3–7 April 2017, pp. 361–370 (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Chen Yang
    • 1
  • Wei Chen
    • 1
  • Bolong Zheng
    • 2
    • 3
  • Tieke He
    • 4
  • Kai Zheng
    • 1
  • Han Su
    • 1
    Email author
  1. 1.Big Data Research CenterUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  3. 3.Department of Computer ScienceAalborg UniversityAalborgDenmark
  4. 4.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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