Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):

D. Bauer, M. Ray, N. Brändle, H. Schrom-Feiertag:
"On Extracting Commuter Information from GPS Motion Data";
Vortrag: 1st International Workshop on Computational Transportation Science-IWCTS 08, Dublin, Irland; 21.07.2008; in: "Proceedings of the First International Workshop on Computational Transportation Science", ACM, (2008).

Commuters rely on realistic and real-time information in order
to optimize the time spent on commuting between home
and work. Delays in (urban) transport and congestion for
individual motorized transport are a major issue for unnecessary
long travel times. While some of these delays occur
randomly, there is also a systematic component. In this paper
we describe a data-driven approach to analyze positions
of an individual collected using GPS to obtain information
on the individualīs typical routes, typical schedules and
the used mode of transport. Furthermore, we propose an
approach to model the probability of an event like missing
a train as a function of time. This allows to optimize the expected commuting time based solely on the commuters motion history. Suitability of the approach is demonstrated in a real world application based on a dataset comprising six weeks of GPS tracks.

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Erstellt aus der Publikationsdatenbank des AIT Austrian Institute of Technology.