Abstract
Road speed prediction is a key point of Intelligent Transport System. Plenty of work have proved the effectiveness and efficiency of neural network in forecasting freeway velocity. However, the missing values are obstacles when applying the widely used trajectory data to neural network. In trajectory data, most roads may not be covered by enough trajectories in a short time. Due to highly sparsity, it will bring extra cost if we first fill missing data then perform training. To solve this issue, we propose a collaborative model that combines LSTM neural network with matrix factorization to reduce sparsity and make prediction simultaneously. We conduct experiments with a sufficient amount of trajectories and the results show that our model outperforms cascaded methods in both MAE and RMSE.
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References
Min, W.L., Wynter, L.: Real-time road traffic prediction with spatio-temporal correlations. Transp. Res. Part C: Emerg. Technol. 19, 606–616 (2011)
Lint, J.W.C.V., Hoogendoorn, S.P., Zuylen, H.J.V.: Accurate freeway travel time prediction with state-space neural networks under missing data. Transp. Res. Part C: Emerg. Technol. 13, 347–369 (2005)
Park, D., Rilett, L.R.: Forecasting multiple-period freeway link travel times using modular neural networks. Transp. Res. Rec.: J. Transp. Res. Board 1617, 163–170 (1998)
Hamilton, J.D.: Time Series Analysis. Princeton University Press, Princeton (1994)
Thiagarajan, A., Ravindranath, L., LaCurts, K., Madden, S., Balakrishnan, H., Toledo, S., Eriksson, J.: VTrack: accurate, energy-aware road traffic delay estimation using mobile phones. In: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, pp. 85–98. ACM (2009)
Qi, Y., Ishak, S.: A hidden Markov model for short term prediction of traffic conditions on freeways. Transp. Res. Part C: Emerg. Technol. 43, 95–111 (2014)
Djuric, N., Radosavljevic, V., Coric, V., Vucetic, S.: Travel speed forecasting by means of continuous conditional random fields. J. Transp. Res. Board 2263, 131–139 (2011)
Funk., S.: Netflix update: Try this at home. http://sifter.org/~simon/journal/20061211.html
Pang, L.X., Chawla, S., Liu, W., Zheng, Y.: On detection of emerging anomalous traffic patterns using GPS data. Data Knowl. Eng. 87, 357–373 (2013)
Wang, Y., Zheng, Y., Xue, Y.: Travel time estimation of a path using sparse trajectories. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 25–34. ACM (2014)
Yuan, J., Zheng, Y., Xie, X., Sun, G.: T-drive: enhancing driving directions with taxi drivers’ intelligence. IEEE Trans. Knowl. Data Eng. 25, 220–232 (2013)
Kumar, K., Parida, M., Katiyar, V.: Short term traffic flow prediction for a non urban highway using artificial neural network. Procedia-Soc. Behav. Sci. 104, 755–764 (2013)
Deka, L., Quddus, M.: Trip-based weighted trajectory matching algorithm for sparse GPS data. In: Transportation Research Board Annual Meeting (2015)
Jenelius, E., Koutsopoulos, H.N.: Travel time estimation for urban road networks using low frequency probe vehicle data. Transp. Res. Part B Methodol. 53, 64–81 (2013)
Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning. Computer Science (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)
Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. eprint arxiv (2014)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Adv. Neural Inf. Process. Syst. 4, 3104–3112 (2014)
Werbos, P.J.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78, 1550–1560 (1990)
Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Springer, Heidelberg (2012)
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., et al.: Tensorflow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2016)
Acknowledgement
The work described in this paper was mainly supported by the National Nature Science Foundation of China (Nos. 61672100, 61375045), the Ph.D Programs Foundation of Ministry of Education of China (No. 20131101120035), the Joint Research Fund in Astronomy under cooperative agreement between the National Natural Science Foundation of China and Chinese Academy of Sciences (No. U1531242), Beijing Natural Science Foundation (Nos. 4162054, 4162027), and the Excellent young scholars research fund of Beijing Institute of Technology.
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Hu, J., Xin, X., Guo, P. (2017). LSTM with Matrix Factorization for Road Speed Prediction. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_29
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DOI: https://doi.org/10.1007/978-3-319-59072-1_29
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