Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):
B. Heilmann, M. Reinthaler, J. Asamer, L. Fehrenbach, J. Pillat, J. Lohmiller, M. Friedrich, K. Schedler:
"Integrating Weather Impact in Travel Demand Models for Private Motorised Transport";
Vortrag: 3rd International Conference on Models and Technologies for ITS 2013,
- 04.12.2013; in: "Proc. 3rd International Conference on Models and Technologies for ITS 2013",
TUDPress, Verlag der Wissenschaften GesmbH,
Travel demand models describing demand‐supply‐interaction of individual Transport are important building blocks of transport planning and traffic management systems. Weather conditions, e.g. top weather or adverse weather like rainfall or snow, can affect
several levels of demand‐supply‐interaction. In order to quantify weather impact on travel demand models, traffic and weather data from an inter‐urban motorway in Bavaria, Germany and the intra‐urban road network in the Austrian capital Vienna have been analysed in the framework of a cooperative research project. By combining absolute and relative thresholds, which were defined relative to local climate summary values, standardised weather classes were defined. On the inter‐urban motorway, weather impact on travel demand differed among weekdays. Demand increase for days with top weather and demand decrease for rainy and snowy days was quantified. In the intra‐urban road network, weather Impact depended mainly on the type of precipitation. Whereas snow decreased passenger car
demand, rain and even heavy rainfall had no significant impact on demand. On the inter‐urban motorway, free speed reduction and capacity reduction due to heavy rain and snowfall was quantified. In the intra‐urban road network, the network flowspeed relation was similar for dry road, wet road combined with rain and slush
combined with snow. Only for snowy road combined with snow, lower network Speeds were observed for given flows. Based on the quantified weather impacts, recommendations are given on how to
integrate weather impact in travel demand models.
Erstellt aus der Publikationsdatenbank des AIT Austrian Institute of Technology.