Research Article
On Extracting Commuter Information from GPS Motion Data
@INPROCEEDINGS{10.4108/ICST.MOBIQUITOUS2008.3881, author={Dietmar Bauer and Markus Ray and Norbert Braendle and Helmut Schrom-Feiertag}, title={On Extracting Commuter Information from GPS Motion Data}, proceedings={1st International ICST Workshop on Computational Transportation Science}, publisher={ACM}, proceedings_a={IWCTS}, year={2010}, month={5}, keywords={commuter information mode detection}, doi={10.4108/ICST.MOBIQUITOUS2008.3881} }- Dietmar Bauer
Markus Ray
Norbert Braendle
Helmut Schrom-Feiertag
Year: 2010
On Extracting Commuter Information from GPS Motion Data
IWCTS
ICST
DOI: 10.4108/ICST.MOBIQUITOUS2008.3881
Abstract
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 individual’s 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.