Authors:
Karl-Heinz Krempels
1
;
Fabian Ohler
1
;
Thomas Osterland
2
and
Christoph Terwelp
1
Affiliations:
1
Informatik 5 Information Systems, RWTH Aachen University, Aachen, Germany, Fraunhofer-Institut für Angewandte Informationstechnik FIT, Sankt Augustin and Germany
;
2
Informatik 5 Information Systems, RWTH Aachen University, Aachen and Germany
Keyword(s):
Mobility Planning, Activity Prediction, Next Location Prediction.
Related
Ontology
Subjects/Areas/Topics:
AI and Creativity
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bayesian Networks
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Intelligent User Interfaces
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
By analyzing the individual travel characteristics of persons, it occurs that most trips are not journeys to other cities or countries but short trips, as the daily trip to work or the weekly meeting at the gym. For those trips, people know the basic conditions, as e.g., the bus driving schedule or the journey duration and it represents more effort to plan the trip beforehand, than just remember the data. But what if there is a problem, like a stalled train or a car crash on the route. Unpredictable ocurrences might be noticed too late and affect the parameters of the trip. A travelling assistant that is able to anticipate regular trips and that warns in case of problems, without requesting dedicated user input might be a solution. In this paper we consider the problem of creating an assistant based on the context information captured from a smartphone. We discuss approaches based on histogram evaluation, a Bayesian network and a multilayer perceptron that allow the prediction of loc
ations and activities given a time and a date. These approaches are benchmarked and compared to each other to find the solution that provides the best results in prediction quality and training speed.
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