Authors:
Tobias Grüner
1
;
Sören Frey
1
;
Jens Nahm
1
and
Dirk Reichardt
2
Affiliations:
1
Independent Researcher, 70563 Stuttgart, Germany
;
2
Baden-Wuerttemberg Cooperative State University Stuttgart (DHBW Stuttgart), Jägerstraße 56, 70174 Stuttgart, Germany
Keyword(s):
Mobility Services, Machine Learning, Prediction, Classification, POI Extraction, Clustering, Location Data.
Abstract:
Mobility services can substantially benefit from incorporating movement behavior information. Models of daily travel routines can facilitate intelligent recommendations of suitable car sharing, ride pooling, or Mobility as a Service (MaaS) offerings, for instance. However, existing approaches that infer regular travel activities from historical location data exhibit several limitations. For example, they often have an insufficient resolution in the spatial and temporal dimension or are restricted to predicting only the next location visit. This paper presents an activity-based approach to model daily travel routines and predict regularities with the help of machine learning (ML). We first extract points of interest (POIs) and corresponding visits from historical location data. Then, regularities for these visits are identified with the help of classification. We validate our work in progress approach using data from voluntary, consenting test subjects (CTS) who agreed to track their
movements. They labeled their own data for each activity with corresponding regularity information. We show that POI visits can already be predicted reliably for the first classes of movements.
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