In the presented case study, travel times for passenger cars (PC) and heavy goods vehicles (HGV) were predicted with a data-driven, hybrid approach, using historical traffic data of the entire high-ranking Austrian road network. In case flow data were available, travel time was predicted with a Kernel predictor searching for similar speed-density patterns. In case of missing flow data, travel time was predicted with deviations from typical historical speed time series. The performed steps in pre-processing traffic data, the hybrid prediction method as well as the results for selected road sections are described and analysed.
A data-driven approach for travel time prediction on motorway sections
01.11.2014
144900 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Motorway travel time prediction based on toll data and weather effect integration
British Library Conference Proceedings | 2010
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