Spatial econometrics of congestion prediction for in- vehicle route guidance

Current research on in-vehicle information system, more specifically dynamic route guidance, is offering the prospect of improved individual trip efficiency by enabling drivers to avoid recurrent and propagated road congestion. One of the next major advances in developing route guidance systems will be the short-term prediction of congestion in road networks. This will inform the driver about the road network conditions one or two hours in advance and assist them to avoid anticipated congestion. This paper discusses the application of spatial econometric modelling in congestion prediction, 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 vehicles acting as traffic probes. 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 econometric modelling techniques to predict congestion is explored. Results are presented at the end. The study will add new content to applied spatial econometrics in the transport field