Abstract
Predicting large-scale transportation network traffic flow has become an important and challenging topic in recent decades. However, accurate traffic flow prediction is still hard to realize. Weather factors such as precipitation in residential areas and tourist destinations affect traffic flow on the surrounding roads. In this paper, we attempt to take precipitation impact into consideration when predicting traffic flow. To realize this idea, we propose a deep traffic flow prediction architecture by introducing a deep bi-directional long short-term memory model, precipitation information, residual connection, regression layer and dropout training method. The proposed model has good ability to capture the deep features of traffic flow. Besides, it can take full advantage of time-aware traffic flow data and additional precipitation data. We evaluate the prediction architecture on taxi trajectory dataset in Chongqing and taxi trajectory dataset in Beijing with corresponding precipitation data from China Meteorological Data Service Center (CMDC). The experiment results demonstrate that the proposed model for traffic flow prediction obtains high accuracy compared with other models.
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Notes
- 1.
China Meteorological Data Service Center (CMDC), http://data.cma.cn/en.
References
Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191–2201 (2014)
Abadi, A., Rajabioun, T., Ioannou, P.A.: Traffic flow prediction for road transportation networks with limited traffic data. IEEE Trans. Intell. Transp. Syst. 16(2), 653–662 (2015)
Ahmed, M.S., Cook, A.R.: Analysis of Freeway Traffic Time-Series Data By Using Box-Jenkins Techniques (1979)
Kamarianakis, Y., Vouton, V.: Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches. Transp. Res. Rec. 1857(1), 74–84 (2003)
Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal arima process: theoretical basis and empirical results. J. Transp. Eng. 129(6), 664–672 (2003)
Jin, X., Zhang, Y., Yao, D.: Simultaneously prediction of network traffic flow based on PCA-SVR. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4492, pp. 1022–1031. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72393-6_121
Leshem, G.: Traffic flow prediction using adaboost algorithm with random forests as a weak learner. In: Enformatika, p. 193 (2011)
Chan, K.Y., Dillon, T.S., Singh, J., Chang, E.: Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and levenberg-cmarquardt algorithm. IEEE Trans. Intell. Transp. Syst. 13(2), 644–654 (2012)
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)
Tian, Y., Pan, L.: Predicting short-term traffic flow by long short-term memory recurrent neural network. In: IEEE International Conference on Smart City/SocialCom/SustainCom, pp. 153–158 (2015)
Wang, Y.Q., Jing, L.: Study of rainfall impacts on freeway traffic flow characteristics. Transp. Res. Procedia 25, 1533–1543 (2017)
Agarwal, M., Maze, T.H., Souleyrette, R.: Impacts of weather on urban freeway traffic flow characteristics and facility capacity. In: Proceedings of the 2005 Mid-Continent Transportation Research Symposium, pp. 18–19 (2005)
Hooper, E., Chapman, L., Quinn, A.: Investigating the impact of precipitation on vehicle speeds on uk motorways. Meteorol. Appl. 21(2), 194–201 (2014)
Ibrahim, A.T., Hall, F.L.: Effect of adverse weather conditions on speed-flow-occupancy relationships (1994)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735 (1997)
Wu, Y., et al.: Bridging the gap between human and machine translation, Google’s neural machine translation system (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hawkins, D.M.: The problem of overfitting. Cheminform 35(19), 1 (2004)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
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Wang, J., Xu, X., He, J., Li, L. (2018). P-DBL: A Deep Traffic Flow Prediction Architecture Based on Trajectory Data. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11062. Springer, Cham. https://doi.org/10.1007/978-3-319-99247-1_21
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DOI: https://doi.org/10.1007/978-3-319-99247-1_21
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