Title :
Adaptive Bayesian network for traffic flow prediction
Author :
Pascale, A. ; Nicoli, M.
Author_Institution :
Dip. Elettron. e Inf., Politec. di Milano, Milan, Italy
Abstract :
Traffic control is essential for the achievement of a sustainable and safe mobility. Monitoring systems for traffic control collect a great amount of data that must be efficiently processed by estimation/prevision models to support operations of traffic management. In this paper we investigate a statistical method for traffic flow forecasting based on graphical modeling of the spatial-temporal evolution of flows. We propose an adaptive Bayesian network in which the network topology changes following the non-stationary characteristics of traffic. Two major stationary areas are recognized as principal phases of traffic flows. Graph optimization is implemented for each phase using mutual information as learning metric. Experimental tests on data provided by the PeMS project in the area of Los Angeles showed that the proposed method can reliably predict traffic dynamics.
Keywords :
belief networks; graph theory; optimisation; statistical analysis; traffic engineering computing; adaptive Bayesian network; graph optimization; graphical modeling; learning metric; principal phases; spatial temporal evolution; statistical method; traffic control; traffic dynamics; traffic flow forecasting; traffic flow prediction; traffic management; Bayesian methods; Complexity theory; Correlation; Forecasting; Joints; Sensors; Vehicles; Bayesian network; Gaussian mixture model; Traffic flow forecasting; graphical model; mutual information;
Conference_Titel :
Statistical Signal Processing Workshop (SSP), 2011 IEEE
Conference_Location :
Nice
Print_ISBN :
978-1-4577-0569-4
DOI :
10.1109/SSP.2011.5967651