Estimation of highway traffic from sparse sensors: Stochastic modeling and particle filtering

Traffic control is essential for the achievement of a sustainable and safe mobility. Monitoring systems deployed over the roads collect a great amount of traffic data that must be efficiently processed by statistical methods to draw traffic macroparameters that are needed for control operations. In this paper we propose a particle filtering approach to estimate the density over a road network starting from noisy and sparse measurements provided by road-embedded sensors. We propose a new Bayesian framework based on the link-node cell transmission model to take into account the stochastic behavior of traffic and the hysteresis phenomenon that are typically observed in real data. Numerical tests show that the estimation method is able to reliably reconstruct the traffic field even in case of very sparse sensor deployments.

[1]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[2]  A. Pascale,et al.  Adaptive Bayesian network for traffic flow prediction , 2011, 2011 IEEE Statistical Signal Processing Workshop (SSP).

[3]  Andreas Hegyi,et al.  Freeway traffic estimation within particle filtering framework , 2007, Autom..

[4]  Ernst Bonek,et al.  Mobility model of vehicle-borne terminals in urban cellular systems , 2003, IEEE Trans. Veh. Technol..

[5]  A. Schadschneider,et al.  Statistical physics of vehicular traffic and some related systems , 2000, cond-mat/0007053.

[6]  Serge P. Hoogendoorn,et al.  State-of-the-art of vehicular traffic flow modelling , 2001 .

[7]  R. Horowitz,et al.  Highway traffic state estimation using improved mixture Kalman filters for effective ramp metering control , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

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

[9]  Michael Schreckenberg,et al.  Traffic and Granular Flow’01 , 2003 .

[10]  Umberto Spagnolini,et al.  Wireless sensor networks for traffic management and road safety , 2012 .

[11]  W. Y. Szeto,et al.  Stochastic cell transmission model (SCTM): A stochastic dynamic traffic model for traffic state surveillance and assignment , 2011 .

[12]  Roberto Horowitz,et al.  Freeway traffic flow simulation using the Link Node Cell transmission model , 2009, 2009 American Control Conference.

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

[14]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

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

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

[17]  Mark Dougherty,et al.  A REVIEW OF NEURAL NETWORKS APPLIED TO TRANSPORT , 1995 .

[18]  Kristine L. Bell,et al.  A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking , 2007 .

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

[20]  Bart De Schutter,et al.  On freeway traffic density estimation for a jump Markov linear model based on Daganzo's cell transmission model , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[21]  M. Nicoli,et al.  Fundamental Performance Limits of TOA-Based Cooperative Localization , 2009, 2009 IEEE International Conference on Communications Workshops.