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

@article{hu08spatial, abstract = {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.}, author = {Hu, J. and Kaparias, I. and Bell, M. G. H.}, booktitle = {Intelligent Transport Systems, IET}, citeulike-article-id = {5706066}, citeulike-linkout-0 = {http://dx.doi.org/10.1049/iet-its:20070062}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5071785}, doi = {10.1049/iet-its:20070062}, journal = {Intelligent Transport Systems, IET}, keywords = {congestion, congestion-preditction, route-guidance, time-series}, number = {2}, pages = {159--167}, posted-at = {2009-09-02 12:28:57}, priority = {2}, title = {Spatial econometrics models for congestion prediction with in-vehicle route guidance}, url = {http://dx.doi.org/10.1049/iet-its:20070062}, volume = {3}, year = {2009} }

TY - JOUR ID - hu08spatial L3 - citeulike-article-id:5706066 N2 - 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. IS - 2 JF - Intelligent Transport Systems, IET EP - 167 TI - Spatial econometrics models for congestion prediction with in-vehicle route guidance VL - 3 T2 - Intelligent Transport Systems, IET SP - 159 KW - congestion KW - congestion-preditction KW - route-guidance KW - time-series AU - Hu, J AU - Kaparias, I AU - Bell, MGH PY - 2009/// UR - http://dx.doi.org/10.1049/iet-its:20070062 ER -