Transportation Research Record 2263 Travel / State-Space Neural Networks / Neural Information Processing Systems / Predicting Travel Times / 6th IEEE Intelligent Transportation Systems / Baseline Predictors Incorporated / Intelligent Transport Systems / J. W. C. S. P. / Recurrent Neural Networks / Freeway Link Travel Times Using Modular Neural Networks / /
Country
Portugal / China / / /
Facility
Wachman Hall / Temple University / Freeway Travel Time Prediction / Sensor Station / University of Minnesota / Freeway Networks / /
IndustryTerm
travel speed forecasting / travel speeds / travel speed prediction / traveler information systems / ordinary least squares algorithm / travel forecasting / traffic monitoring systems / travel time / vehicle guidance systems / travel speed / traffic forecast applications / nearest neighbor algorithm / short-term travel forecasting / /
Organization
Minnesota Department of Transportation Traffic Management Center / Temple University / Royal Statistical Society / Department of Computer and Information Sciences / National Research Council / National Science Foundation / Transportation Research Board / University of Minnesota / Duluth / Transportation Network Modeling Committee / Transportation Research Board of the National Academies / /
Person
Slobodan Vucetic / Conditional Random Fields Nemanja Djuric / Vladan Radosavljevic / Vladimir Coric / / /
Position
extremely suitable model for travel forecasting / temporal CCRF model for travel speed forecasting / Corresponding author / Novel Loglinear Model for Freeway Travel Time Prediction / CCRF model for travel speed forecasting / /
ProvinceOrState
Minnesota / Massachusetts / /
PublishedMedium
Journal of the Royal Statistical Society / Machine Learning / /
Travel Speed Forecasting by Means of Continuous Conditional Random Fields Nemanja Djuric, Vladan Radosavljevic, Vladimir Coric, and Slobodan Vucetic popular because of their ease of implementation and ability to interpre