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 1shallow in architecture;2hand 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.

[1]  Yee Whye Teh,et al.  Rate-coded Restricted Boltzmann Machines for Face Recognition , 2000, NIPS.

[2]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[3]  Shiliang Sun,et al.  The Selective Random Subspace Predictor for Traffic Flow Forecasting , 2007, IEEE Transactions on Intelligent Transportation Systems.

[4]  Shiliang Sun,et al.  Variational Inference for Infinite Mixtures of Gaussian Processes With Applications to Traffic Flow Prediction , 2011, IEEE Transactions on Intelligent Transportation Systems.

[5]  Stephen D. Clark,et al.  Traffic Prediction Using Multivariate Nonparametric Regression , 2003 .

[6]  Meng Ying,et al.  Research of Urban Traffic Flow Forecasting Based on Neural Network , 2009 .

[7]  Yoshua Bengio,et al.  Exploring Strategies for Training Deep Neural Networks , 2009, J. Mach. Learn. Res..

[8]  Geoffrey E. Hinton,et al.  Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes , 2007, NIPS.

[9]  Shiliang Sun,et al.  A bayesian network approach to traffic flow forecasting , 2006, IEEE Transactions on Intelligent Transportation Systems.

[10]  Lee D. Han,et al.  Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions , 2009, Expert Syst. Appl..

[11]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[12]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[13]  Honglak Lee,et al.  Sparse deep belief net model for visual area V2 , 2007, NIPS.

[14]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[15]  Changshui Zhang,et al.  Short-term traffic flow forecasting based on Markov chain model , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[16]  B. G. Ratcliffe,et al.  Short term traffic forecasting using time series methods , 1988 .

[17]  Guojie Song,et al.  An online approach based on locally weighted learning for short-term traffic flow prediction , 2008, GIS '08.

[18]  Michael J Demetsky,et al.  SHORT-TERM TRAFFIC FLOW PREDICTION: NEURAL NETWORK APPROACH , 1994 .

[19]  Mascha C. van der Voort,et al.  Combining kohonen maps with arima time series models to forecast traffic flow , 1996 .