Abstract
To improve predictive accuracy on short-term traffic speed, we proposed a multitask learning neural network (MLNN). MLNN carries out the speed prediction task for three short-terms by the combination of convolution neural network (CNN) and gated recurrent units’ network (GRU), and accomplishes the confidence estimation task on predicted speed with the confidence network. A multitask loss function with weighted sub loss terms for multitask learning is employed. In the experiment, our method was tested on the data set of Shanghai Expressway at 2014. Conventional methods such as auto-regressive integrated moving average (ARIMA) and Gaussian maximum likelihood (GML), and time series models, recurrent neural network (RNN), GRU and long short-term memory (LSTM), were also used to compare. The results show that MLNN with square loss obtained the smallest mean squared error (MSE) on most cases. For four road types, MLNN obtained the overall smallest mean absolute percentage error (MAPE) on these cases. We also proved that as compared to single-term prediction, multitask learning outperformed 12.4% in MSE and 9.91% in MAPE for 10-min and 15-min prediction. To improve the forecast on low speed, MAP-loss term is additionally used in multitask loss function. It efficiently improved the predictive accuracy on low speed. The confidence estimation network gave a 89.93% estimation accuracy on the predicted speed, efficiently avoiding the inaccurate speed prediction.
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References
Avuglah, R.K., Adupoku, K.A., Harris, E.: Application of ARIMA models to road traffic accident cases in Ghana. Int. J. Stat. Appl. 4(5), 233–239 (2014). https://doi.org/10.5923/j.statistics.20140405.03
Chan, K.Y., Dillon, T.S., Singh, J., et al.: Traffic flow forecasting neural networks based on exponential smoothing method. In: Industrial Electronics & Applications. IEEE (2011). https://doi.org/10.1109/iciea.2011.5975612
Lin, W.H.: A Gaussian maximum likelihood formulation for short-term forecasting of traffic flow. In: Proceedings of IEEE Intelligent Transportation Systems, pp. 150–155. IEEE (2001). https://doi.org/10.1109/itsc.2001.948646
Yan, G.X., Ming, T.S.: Integrated traffic flow forecasting and traffic incident detection algorithm based on non-parametric regression. China J. Highw. Transp. 16(1), 82–86 (2003). https://doi.org/10.3321/j.issn:1001-7372.2003.01.020
Castro-Neto, M., Jeong, Y.S., Jeong, M.K., Han, L.D.: Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst. Appl. 36(3), 6164–6173 (2009). https://doi.org/10.1016/j.eswa.2008.07.069
Jia, Y., Wu, J., Du, Y.: Traffic speed prediction using deep learning method. In: Proceedings of IEEE International Conference on Intelligent Transportation Systems, pp: 1217–1222 (2016). https://doi.org/10.1109/itsc.2016.7795712
Ma, X., Tao, Z., Wang, Y.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C Emerg. Technol. 54, 187–197 (2015). https://doi.org/10.1016/j.trc.2015.03.014
Polson, N.G., Sokolov, V.O.: Deep learning for short-term traffic flow prediction. Transp. Res. Part C Emerg. Technol. 79, 1–17 (2017). https://doi.org/10.1016/j.trc.2017.02.024
Tan, J., Wang, S.: Research on prediction model for traffic congestion based on deep learning. Appl. Res. Comput. 32(10), 2951–2954 (2015). https://doi.org/10.3969/j.issn.1001-3695.2015.10.016
Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffic flow prediction. In: 31st Youth Academic Annual Conference of Chinese Association of Automation, pp. 324–328 (2017). https://doi.org/10.1109/yac.2016.7804912
Cui, Z., Ke, R., Wang, Y.: Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction (2018). arXiv:1801.02143
Duan, Y., Lv, Y., Wang, F.Y.: Travel time prediction with LSTM neural network. In: Proceedings of IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). IEEE (2016). https://doi.org/10.1109/itsc.2016.7795686
Cheng, H., Sester, M.: Mixed traffic trajectory prediction using LSTM–based models in shared space. In: Mansourian, A., Pilesjö, P., Harrie, L., van Lammeren, R. (eds.) AGILE 2018. LNGC, pp. 309–325. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78208-9_16
Ma, X., Dai, Z., He, Z., et al.: Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4), 818–825 (2017). https://doi.org/10.3390/s17040818
Zhu, L., Laptev, N.: Deep and confident prediction for time series at uber. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (2017). https://doi.org/10.1109/icdmw.2017.19
Gal, Y.: Uncertainty in deep learning, Ph.D. dissertation, Ph.D thesis, University of Cambridge (2016)
Baxter, J.: A model of inductive bias learning: AI access foundation. J. Artif. Intell. Res. 12 (2000). https://doi.org/10.1613/jair.731
Chen, J., Tan, L., Liu, J., et al.: A convex formulation for learning shared structures from multiple tasks. In: Proceedings of the 26th Annual International Conference on Machine Learning - ICML 2009 (2009). https://doi.org/10.1145/1553374.1553392
Huang, W., Song, G., Hong, H., et al.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191–2201 (2014). https://doi.org/10.1109/tits.2014.2311123
Huang, H.Y., Luo, P.E., Li, M., et al.: Performance evaluation of SUVnet with real-time traffic data. IEEE Trans. Veh. Technol. 56(6), 3381–3396 (2007). https://doi.org/10.1109/tvt.2007.907273
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No. 61872259), the Natural Science Foundation of Jiangsu Province (Grant No. BK20160324) and the Natural Science Foundation of Jiangsu Colleges and Universities (Grant No.16KJB580009).
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Tao, Y., Wang, X., Zhang, Y. (2019). A Multitask Learning Neural Network for Short-Term Traffic Speed Prediction and Confidence Estimation. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning. ICANN 2019. Lecture Notes in Computer Science(), vol 11728. Springer, Cham. https://doi.org/10.1007/978-3-030-30484-3_36
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