Heuristic-Based Journey Planner for Mobility as a Service (MaaS)

Panagiotis Georgakis, Adel Almohammad, Efthimios Bothos, Babis Magoutas, Kostantina Arnaoutaki and Gregoris Mentzas
Additional contact information
Panagiotis Georgakis: School of Architecture and the Built Environment, University of Wolverhampton, Wolverhampton WV10 0JP, UK
Adel Almohammad: School of Architecture and the Built Environment, University of Wolverhampton, Wolverhampton WV10 0JP, UK
Efthimios Bothos: Institute of Computer and Communication Systems, National Technical University of Athens, 157 80 Athens, Greece
Babis Magoutas: Institute of Computer and Communication Systems, National Technical University of Athens, 157 80 Athens, Greece
Kostantina Arnaoutaki: Institute of Computer and Communication Systems, National Technical University of Athens, 157 80 Athens, Greece
Gregoris Mentzas: Institute of Computer and Communication Systems, National Technical University of Athens, 157 80 Athens, Greece

Sustainability, 2020, vol. 12, issue 23, 1-25

Abstract: The continuing growth of urbanisation poses a real threat to the operation of transportation services in large metropolitan areas around the world. As a response, several initiatives that promote public transport and active travelling have emerged in the last few years. Mobility as a Service (MaaS) is one such initiative with the main goal being the provision of a holistic urban mobility solution through a single interface, the MaaS operator. The successful implementation of MaaS requires the support of a technology platform for travellers to fully benefit from the offered transport services. A central component of such a platform is a journey planner with the ability to provide trip options that efficiently integrate the different modes included in a MaaS scheme. This paper presents a heuristic that implements a scenario-based journey planner for users of MaaS. The proposed heuristic provides routes composed of different modes including private cars, public transport, bike-sharing, car-sharing and ride-hailing. The methodological approach for the generation of journeys is explained and its implementation using a microservices architecture is presented. The implemented system was trialled in two European cities and the analysis of user satisfaction results reveal good overall performance.

Keywords: mobility as a service (MaaS); dynamic journey planning; personalised routes recommendation; scenario-based modelling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/23/10140/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/23/10140/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:23:p:10140-:d:456856

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().