High-Order Gaussian Process Dynamical Models for Traffic Flow Prediction

Traffic flow prediction, which predicts the future flow using historic flows, is an important task in intelligent transportation systems (ITS). Efficient and accurate models for traffic flow prediction greatly contribute to the development of ITS. In this paper, we adopt the Gaussian process dynamical model (GPDM) to a fourth-order GPDM, which is more suitable for modeling traffic flow data. Specifically, the latent variables in the fourth-order GPDM is a fourth-order Markov Gaussian process, and the weighted k-NN is incorporated in the model to predict latent variables for efficient prediction. After training the model, the future flow is estimated by the average of the results predicted by the fourth-order GPDM and k-NN. Compared with other popular methods, the proposed method performs best and yields significant improvements of prediction performance.

[1]  Edmund S. Yu,et al.  Traffic prediction using neural networks , 1993, Proceedings of GLOBECOM '93. IEEE Global Telecommunications Conference.

[2]  David J. Fleet,et al.  Gaussian Process Dynamical Models , 2005, NIPS.

[3]  Shiliang Sun,et al.  Revisiting Gaussian Process Dynamical Models , 2015, IJCAI.

[4]  Rini Akmeliawati,et al.  Gaussian Process Dynamical Models for hand gesture interpretation in Sign Language , 2011, Pattern Recognit. Lett..

[5]  David J. Fleet,et al.  3D People Tracking with Gaussian Process Dynamical Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[6]  David J. Fleet,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .

[7]  Fei-Yue Wang,et al.  Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications , 2010, IEEE Transactions on Intelligent Transportation Systems.

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

[9]  W. Marsden.  I and J , 2012 .

[10]  Billy M. Williams,et al.  MODELING AND FORECASTING VEHICULAR TRAFFIC FLOW AS A SEASONAL STOCHASTIC TIME SERIES PROCESS , 1999 .

[11]  Fei-Yue Wang,et al.  Building an intellectual highway for ITS research and development , 2010 .

[12]  Shiliang Sun,et al.  Neural network multitask learning for traffic flow forecasting , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[13]  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.

[14]  Gustav Eje Henter,et al.  Gaussian process dynamical models for nonparametric speech representation and synthesis , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  Ying Sun,et al.  Gaussian Processes for Short-Term Traffic Volume Forecasting , 2010 .

[16]  I Okutani,et al.  Dynamic prediction of traffic volume through Kalman Filtering , 1984 .

[17]  Gary A. Davis,et al.  Nonparametric Regression and Short‐Term Freeway Traffic Forecasting , 1991 .

[18]  Neil D. Lawrence,et al.  Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data , 2003, NIPS.

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

[20]  Frank Chongwoo Park,et al.  Grasp motion learning with Gaussian Process Dynamic Models , 2012, 2012 IEEE International Conference on Automation Science and Engineering (CASE).

[21]  Tom Thomas,et al.  Predictions of Urban Volumes in Single Time Series , 2010, IEEE Transactions on Intelligent Transportation Systems.

[22]  Paolo Frasconi,et al.  Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning , 2013, IEEE Transactions on Intelligent Transportation Systems.

[23]  Shiliang Sun.  Traffic flow forecasting based on multitask ensemble learning , 2009, GEC '09.

[24]  Phillip Boyle,et al.  Gaussian Processes for Regression and Optimisation , 2007 .

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

[26]  Hyunsin Park.  Gaussian Process Dynamical Models for Phoneme Classification , 2011 .

[27]  Markos Papageorgiou,et al.  A Simplified Estimation Scheme for the Number of Vehicles in Signalized Links , 2010, IEEE Transactions on Intelligent Transportation Systems.