Deep Architecture for Traffic Flow Prediction
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.
KeywordsDeep Learning Deep Belief Nets Traffic Flow Prediction
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- 6.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)Google Scholar
- 8.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)Google Scholar
- 9.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)Google Scholar
- 10.Highway Capacity Manual. Highway capacity manual (2000)Google Scholar
- 11.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)Google Scholar
- 13.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)Google Scholar
- 14.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)Google Scholar
- 15.Smith, B.L., Demetsky, M.J.: Short-term traffic flow prediction: Neural network approach. Transportation Research Record (1453) (1994)Google Scholar
- 19.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)Google Scholar
- 21.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)Google Scholar