Advertisement

Predicting Passengers in Public Transportation Using Smart Card Data

  • Mengyu Dou
  • Tieke He
  • Hongzhi Yin
  • Xiaofang Zhou
  • Zhenyu Chen
  • Bin Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9093)

Abstract

Transit prediction has long been a hot research problem, which is central to the public transport agencies and operators, as evidence to support scheduling and urban planning. There are several previous work aiming at transit prediction, but they are all from the macro perspective. In this paper, we study the prediction of individuals in the context of public transport. Existing research on the prediction of individual behaviour are mostly found in information retrieval and recommender systems, leaving it untouched in the area of public transport. We propose a NLP based back-propagation neural network for the prediction job in this paper. Specifically, we adopt the concept of “bag of words” to build user profile, and use the result of clustering as input of back-propagation neural network to generate predictions. To illustrate the effectiveness of our method, we conduct an extensive set of experiments on a dataset from public transport fare collecting system. Our detailed experimental evaluation demonstrates that our method gets good performance on predicting public transport individuals.

Keywords

Transportation Prediction Smart card Bag-of-words Back-propagation neural network 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cao, Z., Wang, S., Forestier, G., Puissant, A., Eick, C.F.: Analyzing the composition of cities using spatial clustering. In: UrbComp 2013: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, New York, USA, pp. 14:1–14:8 (2013)Google Scholar
  2. 2.
    Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. PNAS 106(36), 15274–15278 (2009)CrossRefGoogle Scholar
  3. 3.
    Ganti, R., Srivatsa, M., Ranganathan, A., Han, J.: Inferring human mobility patterns from taxicab location traces. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 459–468. ACM (2013)Google Scholar
  4. 4.
    He, W., Li, D., Zhang, T., An, L., Guo, M., Chen, G.: Mining regular routes from gps data for ridesharing recommendations. In: UrbComp 2012: Proceedings of the ACM SIGKDD International Workshop on Urban Computing, New York, USA, pp. 79–86 (2012)Google Scholar
  5. 5.
    Hecht-Nielsen, R.: Theory of the backpropagation neural network. In: International Joint Conference on Neural Networks, IJCNN, pp. 593–605. IEEE (1989)Google Scholar
  6. 6.
    Ishak, S., Kotha, P., Alecsandru, C.: Optimization of dynamic neural network performance for short-term traffic prediction. Transportation Research Record: Journal of the Transportation Research Board 1836(1), 45–56 (2003)CrossRefGoogle Scholar
  7. 7.
    Jiang, S., Jr., J.F., Gonzalez, M.C.: Discovering urban spatial-temporal structure from human activity patterns. In: UrbComp 2012, pp. 95–102, August 2012Google Scholar
  8. 8.
    Min, W., Wynter, L.: Real-time road traffic prediction with spatio-temporal correlations. Transportation Research Part C: Emerging Technologies 19(4), 606–616 (2011)CrossRefGoogle Scholar
  9. 9.
    Paulos, E., Goodman, E.: The familiar stranger: anxiety, comfort, and play in public places. In: pp. 223–230. ACM, New York (2004)Google Scholar
  10. 10.
    Pelletier, M.-P., Trépanier, M., Morency, C.: Smart card data use in public transit: A literature review. Transportation Research Part C: Emerging Technologies 19(4), 557–568 (2011)CrossRefGoogle Scholar
  11. 11.
    Wang, J., Mao, Y., Li, J., Li, C., Xiong, Z., Wang, W.-X.: Predictability of road traffic and congestion in urban areas. arXiv preprint arXiv:1407.1871 (2014)
  12. 12.
    Xue, A.Y., Zhang, R., Zheng, Y., Xie, X., Yu, J., Tang, Y.: Desteller: a system for destination prediction based on trajectories with privacy protection. In: Proceedings of the VLDB Endowment, vol. 6 (2013)Google Scholar
  13. 13.
    Yuan, J., Zheng, Y., Xie, X.: Discovering regions of different functions in a city using human mobility and pois. In: KDD 2012, pp. 186–194, August 2012Google Scholar
  14. 14.
    Zheng, Y., Liu, Y., Yuan, J., Xie, X.: Urban computing with taxicabs. In: UbiComp 2011, pp. 89–98, September 2011Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mengyu Dou
    • 1
  • Tieke He
    • 1
  • Hongzhi Yin
    • 2
  • Xiaofang Zhou
    • 2
  • Zhenyu Chen
    • 1
  • Bin Luo
    • 1
  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbane, St. LuciaAustralia

Personalised recommendations