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MultiModal Route Planning in Mobility as a Service

Published:14 October 2019Publication History

ABSTRACT

Mobility 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. Therefore, 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).

References

  1. Jönson G., & Tengström E. (2006). Urban Transport Development: A Complex Issue. Springer Science & Business Media.Google ScholarGoogle Scholar
  2. Durand, A., Harms, L., Hoogendoorn-Lanser, S., & Zijlstra, T. (2018). Mobility-as-a-Service and changes in travel preferences and travel behaviour: a literature review. KiM Netherlands Institute for Transport Policy Analysis.Google ScholarGoogle Scholar
  3. Kamargianni, M., & Matyas, M. (2017). The business ecosystem of mobility-as-a-service. In transportation research board (Vol. 96). Transportation Research Board.Google ScholarGoogle Scholar
  4. Ştefănescu, P., Mocan, M., Ştefănescu, W., & Neculai, P. V. (2014). Trip Planners Used in Public Transportation. Case Study on the City of Timişoara. Procedia-Social and Behavioral Sciences, 124, 142-148.Google ScholarGoogle ScholarCross RefCross Ref
  5. Rahaman, M. S., Mei, Y., Hamilton, M., & Salim, F. D. (2017). CAPRA: A contour-based accessible path routing algorithm. Information Sciences, 385, 157-173.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Yu, H., & Lu, F. (2012). A multi-modal route planning approach with an improved genetic algorithm. Advances in Geo-Spatial Information Science, 193.Google ScholarGoogle Scholar
  7. Liu, L. (2011). Data model and algorithms for multimodal route planning with transportation networks (Doctoral dissertation, Technische Universität München).Google ScholarGoogle Scholar
  8. Clauss, T., & Döppe, S. (2016). Why do urban travelers select multimodal travel options: A repertory grid analysis. Transportation Research Part A: Policy and Practice, 93, 93-116.Google ScholarGoogle ScholarCross RefCross Ref
  9. Kramers, A., Höjer, M., Lövehagen, N., & Wangel, J. (2014). Smart sustainable cities–Exploring ICT solutions for reduced energy use in cities. Environmental modelling & software, 56, 52-62.Google ScholarGoogle Scholar
  10. Hrncir, J., & Jakob, M. (2013). (2013). Generalised time-dependent Graphs for fully multimodal journey planning. Paper presented at the Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2013), The Netherlands.Google ScholarGoogle Scholar
  11. Prandtstetter, M., Straub, M., & Puchinger, J. (2013). On the way to a multi-modal energy-efficient route. Paper presented at the 39th Annual Conference of the IEEE Industrial Electronics Society, IECON 2013, Vienna.Google ScholarGoogle Scholar
  12. Bast, H., Delling, D., Goldberg, A., Müller-Hannemann, M., Pajor, T., Sanders, P., Wagner, D., Werneck, R. F. (2016). Route planning in transportation networks. Algorithm Engineering, Springer, 19-80.Google ScholarGoogle Scholar
  13. Gillies, S., Butler, H., Daly, M., Doyle, A., & Schaub, T. (2016). The GeoJSON Format. coordinates, 102, 0-5.Google ScholarGoogle Scholar
  14. Lamsfus, C., Wang, D., Alzua-Sorzabal, A., & Xiang, Z. (2015). Going mobile defining context for on-the-go travelers. Journal of Travel Research, 54(6), 691-701.Google ScholarGoogle ScholarCross RefCross Ref
  15. Newson, P., Krumm, J. (2009), Hidden Markov Map Matching Through Noise and Sparseness, 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS 2009), November 4-6, Seattle, WA | November 2009Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Gretzel, U., Hwang, Y. H., & Fesenmaier, D. R. (2012). Informing destination recommender systems design and evaluation through quantitative research. International Journal of Culture, Tourism and Hospitality Research, 6(4), 297-315.Google ScholarGoogle ScholarCross RefCross Ref
  17. Benediktsson J.A. and I. Kanellopoulos. Classification of multisource and hyperspectral data based on decision. IEEE Transactions On Geoscience And Remote Sensing, 1999.Conference Name:ACM Woodstock conference.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Other conferences
    WI '19 Companion: IEEE/WIC/ACM International Conference on Web Intelligence - Companion Volume
    October 2019
    326 pages

    Copyright © 2019 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 14 October 2019

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    Overall Acceptance Rate118of178submissions,66%

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