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.
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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)
Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. PNAS 106(36), 15274–15278 (2009)
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)
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)
Hecht-Nielsen, R.: Theory of the backpropagation neural network. In: International Joint Conference on Neural Networks, IJCNN, pp. 593–605. IEEE (1989)
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)
Jiang, S., Jr., J.F., Gonzalez, M.C.: Discovering urban spatial-temporal structure from human activity patterns. In: UrbComp 2012, pp. 95–102, August 2012
Min, W., Wynter, L.: Real-time road traffic prediction with spatio-temporal correlations. Transportation Research Part C: Emerging Technologies 19(4), 606–616 (2011)
Paulos, E., Goodman, E.: The familiar stranger: anxiety, comfort, and play in public places. In: pp. 223–230. ACM, New York (2004)
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)
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)
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)
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 2012
Zheng, Y., Liu, Y., Yuan, J., Xie, X.: Urban computing with taxicabs. In: UbiComp 2011, pp. 89–98, September 2011
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Dou, M., He, T., Yin, H., Zhou, X., Chen, Z., Luo, B. (2015). Predicting Passengers in Public Transportation Using Smart Card Data. In: Sharaf, M., Cheema, M., Qi, J. (eds) Databases Theory and Applications. ADC 2015. Lecture Notes in Computer Science(), vol 9093. Springer, Cham. https://doi.org/10.1007/978-3-319-19548-3_3
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DOI: https://doi.org/10.1007/978-3-319-19548-3_3
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