Adaptive Kalman filtering for multi-step ahead traffic flow prediction

Given the importance of continuous traffic flow forecasting in most of Intelligent Transportation Systems (ITS) applications, where every new traffic data become available in every few minutes or seconds, the main objective of this study is to perform a multi-step ahead traffic flow forecasting that can meet a trade-off between accuracy, low computational load, and limited memory capacity. To this aim, based on adaptive Kalman filtering theory, two forecasting approaches are proposed. We suggest solving a multi-step ahead prediction problem as a filtering one by considering pseudo-observations coming from the averaged historical flow or the output of other predictors in the literature. For taking into account the stochastic modeling of the process and the current measurements we resort to an adaptive scheme. The proposed forecasting methods are evaluated by using measurements of the Grenoble south ring.

[1]  C. Lan.  A recursive traffic flow predictor based on dynamic generalized linear model framework , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[2]  Bart van Arem,et al.  Recent advances and applications in the field of short-term traffic forecasting. , 1997 .

[3]  Jingxin Xia.  DYNAMIC FREEWAY TRAVEL TIME PREDICTION USING SINGLE LOOP DETECTOR AND INCIDENT DATA , 2006 .

[4]  Mark Dougherty,et al.  SHOULD WE USE NEURAL NETWORKS OR STATISTICAL MODELS FOR SHORT TERM MOTORWAY TRAFFIC FORECASTING , 1997 .

[5]  Baher Abdulhai,et al.  Short Term Freeway Traffic Flow Prediction Using Genetically-Optimized Time-Delay-Based Neural Networks , 1999 .

[6]  W. Lam,et al.  Comparison of Four Modeling Techniques for Short-Term AADT Forecasting in Hong Kong , 2003 .

[7]  Billy M. Williams,et al.  Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models , 1998 .

[8]  Wei-Hua Lin.  A Gaussian maximum likelihood formulation for short-term forecasting of traffic flow , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[9]  Michael J Demetsky,et al.  Multiple-Interval Freeway Traffic Flow Forecasting , 1996 .

[10]  Michael J Demetsky,et al.  TRAFFIC FLOW FORECASTING: COMPARISON OF MODELING APPROACHES , 1997 .

[11]  Carlos Canudas-de-Wit,et al.  Optimal balancing of road traffic density distributions for the Cell Transmission Model , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[12]  Brian Lee Smith,et al.  PARAMETRIC AND NONPARAMETRIC TRAFFIC VOLUME FORECASTING , 2000 .

[13]  Biao Huang,et al.  System Identification , 2000, Control Theory for Physicists.

[14]  Carlos Canudas-de-Wit,et al.  Analysis and design of equilibrium points for the Cell-Transmission Traffic Model , 2012, 2012 American Control Conference (ACC).

[15]  Andrew P. Sage,et al.  System Identification , 1971 .

[16]  Matthew G. Karlaftis,et al.  A multivariate state space approach for urban traffic flow modeling and prediction , 2003 .

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

[18]  Eleni I. Vlahogianni,et al.  Short‐term traffic forecasting: Overview of objectives and methods , 2004 .

[19]  Sherif Ishak,et al.  Optimizing traffic prediction performance of neural networks under various topological, input, and traffic condition settings , 2004 .

[20]  Andrew P. Sage,et al.  Adaptive filtering with unknown prior statistics , 1969 .

[21]  Billy M. Williams,et al.  Comparison of parametric and nonparametric models for traffic flow forecasting , 2002 .

[22]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[23]  Mecit Cetin,et al.  Short-Term Traffic Flow Prediction with Regime Switching Models , 2006 .

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

[25]  Zhirui Ye,et al.  Short‐Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition , 2007, Comput. Aided Civ. Infrastructure Eng..

[26]  Joe Whittaker,et al.  TRACKING AND PREDICTING A NETWORK TRAFFIC PROCESS , 1997 .

[27]  Yf F. Tang,et al.  Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census , 2006 .

[28]  Roland Chrobok,et al.  Different methods of traffic forecast based on real data , 2004, Eur. J. Oper. Res..

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

[30]  Mark Dougherty,et al.  SHORT TERM INTER-URBAN TRAFFIC FORECASTS USING NEURAL NETWORKS , 1997 .

[31]  Stephen D. Clark,et al.  THE USE OF NEURAL NETWORKS AND TIME SERIES MODELS FOR SHORT TERM TRAFFIC FORECASTING: A COMPARATIVE STUDY , 1993 .

[32]  Maurice Aron,et al.  TRAFFIC MANAGEMENT. RTS ENGLISH ISSUE NUMBER 6. ATHENA: A METHOD FOR SHORT-TERM INTER-URBAN MOTORWAY TRAFFIC FORECASTING , 1991 .

[33]  B. Tapley,et al.  Adaptive sequential estimation with unknown noise statistics , 1976 .