Abstract
Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail at providing favorable results duo to 1)shallow in architecture;2)hand engineered in features. In this paper, we propose a deep architecture consists of two parts: a Deep Belief Network in the bottom and a regression layer on the top. The Deep Belief Network employed here is for unsupervised feature learning. It could learn effective features for traffic flow prediction in an unsupervised fashion which has been examined effective for many areas such as image and audio classification. To the best of our knowledge, this is the first work of applying deep learning approach to transportation research. Experiments on two types of transportation datasets show good performance of our deep architecture. Abundant experiments show that our approach could achieve results over state-of-the-art with near 3% improvements. Good results demonstrate that deep learning is promising in transportation research.
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References
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 Systems with Applications 36(3), 6164–6173 (2009)
Clark, S.: Traffic prediction using multivariate nonparametric regression. Journal of Transportation Engineering 129(2), 161–168 (2003)
Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Computation 14(8), 1771–1800 (2002)
Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Computation 18(7), 1527–1554 (2006)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25, pp. 1106–1114 (2012)
Larochelle, H., Bengio, Y., Louradour, J., Lamblin, P.: Exploring strategies for training deep neural networks. The Journal of Machine Learning Research 10, 1–40 (2009)
Lee, H., Ekanadham, C., Ng, A.: Sparse deep belief net model for visual area v2. In: Advances in Neural Information Processing Systems, vol. 20, pp. 873–880 (2008)
Ma, J., Li, X.-D., Meng, Y.: Research of urban traffic flow forecasting based on neural network. Acta Electronica Sinica 37(5), 1092–1094 (2009)
Highway Capacity Manual. Highway capacity manual (2000)
Mohamed, A.-R., Dahl, G., Hinton, G.: Deep belief networks for phone recognition. In: NIPS Workshop on Deep Learning for Speech Recognition and Related Applications (2009)
Moorthy, C.K., Ratcliffe, B.G.: Short term traffic forecasting using time series methods. Transportation Planning and Technology 12(1), 45–56 (1988)
Salakhutdinov, R., Hinton, G.: Using deep belief nets to learn covariance kernels for gaussian processes. In: Advances in Neural Information Processing Systems, vol. 20, pp. 1249–1256 (2008)
Shuai, M., Xie, K., Pu, W., Song, G., Ma, X.: An online approach based on locally weighted learning for short-term traffic flow prediction. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 45. ACM (2008)
Smith, B.L., Demetsky, M.J.: Short-term traffic flow prediction: Neural network approach. Transportation Research Record (1453) (1994)
Sun, S., Xu, X.: Variational inference for infinite mixtures of gaussian processes with applications to traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems 12(2), 466–475 (2011)
Sun, S., Zhang, C.: The selective random subspace predictor for traffic flow forecasting. IEEE Transactions on Intelligent Transportation Systems 8(2), 367–373 (2007)
Sun, S., Zhang, C., Yu, G.: A bayesian network approach to traffic flow forecasting. IEEE Transactions on Intelligent Transportation Systems 7(1), 124–132 (2006)
Teh, Y.W., Hinton, G.E.: Rate-coded restricted boltzmann machines for face recognition. In: Advances in Neural Information Processing Systems, pp. 908–914 (2001)
Van Der Voort, M., Dougherty, M., Watson, S.: Combining kohonen maps with arima time series models to forecast traffic flow. Transportation Research Part C: Emerging Technologies 4(5), 307–318 (1996)
Yu, G., Hu, J., Zhang, C., Zhuang, L., Song, J.: Short-term traffic flow forecasting based on markov chain model. In: Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 208–212. IEEE (2003)
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Huang, W., Hong, H., Li, M., Hu, W., Song, G., Xie, K. (2013). Deep Architecture for Traffic Flow Prediction. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53917-6_15
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DOI: https://doi.org/10.1007/978-3-642-53917-6_15
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