Axhausen, Kay W.
- Working Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
Analysis of long-distance travel demand has become more relevant in recent times. The reason is the growing share of traffic produced by journeys to remote activities, which are not part of daily life. In today?s mobile world, these journeys are responsible for almost 50 percent of overall traffic. Traditionally, surveys have been used to gather data needed for the analysis of travel demand. Due to the high response burden and memory issues, respondents are known to underreport the number of journeys. Thus, alternative data sources are becoming more important. These sources collect the data passively, e.g. using GPS or GSM networks. The limitation of passively collected data is the lack of semantic information, especially trip purposes. Additionally, socio-demographic information is also missing making it difficult to impute the purpose. This paper shows how one can predict the tour purpose without the need of socio-demographic information. The solution extends the well known random forest approach. Attributes of the tours are used in order to overcome the lack of personal information. The training set for the algorithm was taken from a national travel survey Show more
External linksSearch via SFX
Journal / seriesArbeitsberichte Verkehrs- und Raumplanung
PublisherIVT, ETH Zurich
Organisational unit03521 - Axhausen, Kay W. / Axhausen, Kay W.
02226 - NSL - Netzwerk Stadt und Landschaft / NSL - Network City and Landscape
02655 - Netzwerk Stadt und Landschaft D-ARCH
Related publications and datasets
Is previous version of: http://hdl.handle.net/20.500.11850/204153
MoreShow all metadata