Floating Data Applications for Traffic Demand Modelling

Solving urban transportation issues is a big concern of traffic planners. In order to better understand human mobility behaviour, travel surveys are conducted. They give insights into travel demand, route choice and used transportation modes. Traditionally, interviewers would ask households about their mobility habits by phone or let them fill in a questionnaire. Problems such as high costs, low-response rates and erroneous answers could be avoided by analysing travel behaviour from Floating Car Data (FCD) and Floating Smartphone Data (FSD).

In this doctoral project, a methodology to estimate origin-destination matrices with transport mode information from local detector counts, FCD and FSD is developed. This includes a machine learning approach to infer transportation modes from FSD. The methodology is evaluated using FCD from a motorway network in Duisburg and using FSD from Braunschweig city center. Both networks are modeled in a microscopic traffic simulation to assess different traffic situations.