A data-driven approach for travel time prediction on motorway sections
B Heilmann, H Koller, J Asamer… - … Vehicles and Expo …, 2014 - ieeexplore.ieee.org
B Heilmann, H Koller, J Asamer, M Reinthaler, M Aleksa, S Breuss, G Richter
2014 International Conference on Connected Vehicles and Expo (ICCVE), 2014•ieeexplore.ieee.orgIn 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 …
(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 …
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.
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