Spatial econometrics models for congestion prediction with in-vehicle route guidance

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The congestion dependence relationship among links using microsimulation is explored, based on data from a real road network. The work is motivated by recent innovations to improve the reliability of Dynamic Route Guidance (DRG) systems. The reliability of DRG systems can be significantly enhanced by adding a function to predict the congestion in the road network. The application of spatial econometrics modelling to congestion prediction is also explored, by using historical traffic message channel (TMC) data stored in the vehicle navigation unit. The nature of TMC data is in the form of a time series of geo-referenced congestion warning messages, which is generally collected from various traffic sources. The prediction of future congestion could be based on the previous year of TMC data. Synthetic TMC data generated by microscopic traffic simulation for the network of Coventry are used in this study. The feasibility of using spatial econometrics modelling techniques to predict congestion is explored. The results are presented at the end.

IET Intelligent Transport Systems
Simon Hu