Research Article
Personalized multi-modal route planning: a preference-measurement and learning-based approach
@INPROCEEDINGS{10.4108/icst.mobiquitous.2014.257943, author={Jianwei Zhang and Theo Arentze}, title={Personalized multi-modal route planning: a preference-measurement and learning-based approach}, proceedings={Workshop Indoor/outdoor Location Based Services}, publisher={ICST}, proceedings_a={I-LOCATE}, year={2014}, month={11}, keywords={traveler information systems multi-modal route planning travel preferences stated choice experiments multi-criteria costs functions bayesian learning}, doi={10.4108/icst.mobiquitous.2014.257943} }- Jianwei Zhang
Theo Arentze
Year: 2014
Personalized multi-modal route planning: a preference-measurement and learning-based approach
I-LOCATE
ICST
DOI: 10.4108/icst.mobiquitous.2014.257943
Abstract
Personalized routing recommendation is receiving increasing attention in both academia and engineering. The methodology of how to customize multi-modal routing recommendation to personal preferences of users however is still subject of current research. In the context of the EU FP7 i-Tour project, we developed a set of approaches to solve this problem which are focused on multi-criteria link costs functions, measurement of users’ travel preferences and real-time learning of user preferences. The components developed have been successfully integrated and tested as part of the i-Tour prototype system. In this paper we provide an overview of the methods and results.
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