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.
|Titel||Proceedings 2014 International Conference on Connected Vehicles and Expo (ICCVE2014)|
|Publikationsstatus||Veröffentlicht - 2014|
|Veranstaltung||2014 International Conference on Connected Vehicles and Expo (ICCVE 2014) - |
Dauer: 3 Juli 2014 → 7 Nov. 2014
|Konferenz||2014 International Conference on Connected Vehicles and Expo (ICCVE 2014)|
|Zeitraum||3/07/14 → 7/11/14|
- Ehemaliges Research Field - Mobility Systems