EditorsBarnaghi, Payam M
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AbstractMobility as a Service (MaaS) is a new approach for multimodal transportation in smart cities which refers to the seamless integration of various forms of transport services accessible through one single digital platform. In a MaaS environment there can be a multitude of multi modal options to reach a destination which are derived from combinations of available transport services. Terefore, route planning functionalities in the MaaS era need to be able to generate multi-modal routes using constraints related to a user's modal allowances, service provision and limited user preferences (e.g. mode exclusions) and suggest to the traveller the routes that are relevant for specific trips as well as aligned to her/his preferences. In this paper, we describe an architecture for a MaaS multi-modal route planner which integrates i) a dynamic journey planner that aggregates unimodal routes from existing route planners (e.g. Google directions or Here routing), enriches them with innovative mobility services typically found in MaaS schemes, and converts them to multimodal options, while considering aspects of transport network supply and ii) a route recommender that filters and ranks the available routes in an optimal manner, while trying to satisfy travellers' preferences as well as requirements set by the MaaS operator (e.g. environmental friendliness of the routes or promotion of specific modes of transport).
CitationGeorgakis, P., Almohammad, A., Bothos, E. et al. (2019) MultiModal route planning in mobility as a service, in Barnaghi, P. et al (eds.) Proceedings 2019 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WI 2019 Companion), Thessaloniki, Greece 13th–17th October 2019, pp. 283–291.
JournalProceedings 2019 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WI 2019 Companion)
DescriptionThis is an accepted manuscript of an article published by ACM in Proceedings 2019 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WI 2019 Companion) in October 2019, available online: https://doi.org/10.1145/3358695.3361843 The accepted version of the publication may differ from the final published version.
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/