Let's Connect
Follow Us
Watch Us
(+385) 1 2380 262
journal.prometfpz.unizg.hr
Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

Login Submit

PDF
PUBLISHED
30.09.2022
DOI
https://doi.org/10.7307/ptt.v34i5.4126
ISSUE
2022 (Vol 34), Issue 5
LICENSE
Copyright (c) 2022 Yinying He, Csaba Csiszár
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Correlation Analysis Method of Customisation and Semi-Personalisation in Mobility as a Service

Authors:Yinying He, Csaba Csiszár

Abstract

Mobility as a Service (MaaS) has been proposed as a user-centric, data-driven and personalised service. However, full personalisation is not available yet. Customisation settings are developed in mobile applications, and several semi-personalised functionalities are also involved. The quantitative analysis of relation between these two could be the reference for further development tendency of interface functions in mobile applications. Thus, the research objective is identified as: the quantitative correlation analysis between semi-personalisation functionalities and customisation settings. Accordingly, the multi-criteria qualitative analysis method is applied to identify the assessment aspects regarding mobile applications. The scoring method is also introduced. Then the correlation quantitative analysis method is applied to calculate the correlation coefficient. We have assessed 25 MaaS applications regarding determined aspects. The correlation coefficients for each application together with the overall coefficient are calculated, the assessment results are summarised, and the correlation tendency is interpreted. According to assessment results, the correlation between customisation settings and semi-personalisation is not strong at current stage. Selected MaaS mobile applications are customisation setting oriented applications. Fewer manual selections are expected in further personalised services. Our results facilitate development of further personalised functions in MaaS mobile applications.

Keywords:Mobility as a Service, mobile application, customisation, semi-personalisation, correlation analysis

References

  1. [1] Lom M, Pribyl O. Smart city model based on systems theory. International Journal of Information Management. 2021;56: 102092. doi: 10.1016/j.ijinfomgt.2020.102092.
  2. [2] Ismagilova E, Hughes L, Dwivedi YK, Raman KR. Smart cities: Advances in research—an information systems perspective. International Journal of Information Management. 2019;47: 88-100. doi: 10.1016/j.ijinfomgt.2019.01.004.
  3. [3] Aguiléra A. Smartphone and individual travel behavior. In: Aguilera A, Boutueil V. Urban Mobility and the Smartphone: Transportation, Travel Behavior and Public Policy. 2018. p. 1-37. doi: 10.1016/B978-0-12-812647-9.00001-9.
  4. [4] Boutueil V. New mobility services. In: Aguilera A, Boutueil V. Urban Mobility and the Smartphone: Transportation, Travel Behavior and Public Policy. 2018. p. 39-78. doi: 10.1016/B978-0-12-812647-9.00002-0
  5. [5] Miskolczi M, Földes D, Munkácsy A, Jászberényi M. Urban mobility scenarios until the 2030s. Sustainable Cities and Society. 2021;72: 103029. doi: 10.1016/j.scs.2021.103029.
  6. [6] Chrétien J, Le Néchet F, Leurent F, Yin B. Using mobile phone data to observe and understand mobility behavior, territories, and transport usage. In: Aguilera A, Boutueil V. Urban Mobility and the Smartphone: Transportation, Travel Behavior and Public Policy. 2018. p. 79-141. doi: 10.1016/B978-0-12-812647-9.00003-2.
  7. [7] Anshari M, Almunawar MN, Lim SA, Al-Mudimigh A. Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics. 2019;15(2): 94-101. doi: 10.1016/j.aci.2018.05.004.
  8. [8] Schofer JL, Mahmassani HS. Mobility 2050. A vision for transportation infrastructure. 2016.
  9. [9] Esztergár-Kiss D, Kerényi T, Mátrai T, Aba A. Exploring the MaaS market with systematic analysis. European Transport Research Review. 2020;12(1): 1-16. doi: 10.1186/s12544-020-00465-z.
  10. [10] Bandeira JM, et al. Multidimensional indicator of MaaS systems performance. Transportation Research Procedia. 2022;62: 491-500. doi: 10.1016/j.trpro.2022.02.061.
  11. [11] A brief history of MaaS global, the company behind the Whim app. https://whimapp.com/helsinki/en/history-of-maas-global/ [Accessed 4th Mar. 2022].
  12. [12] About UbiGo. https://www.fluidtime.com/en/ubigo/ [Accessed 5th Mar. 2022].
  13. [13] Sochor J, Karlsson IM, Strömberg H. Trying out mobility as a service: Experiences from a field trial and implications for understanding demand. Transportation Research Record. 2016;2542(1): 57-64. doi: 10.3141/2542-07.
  14. [14] Hensher DA, Ho CQ, Reck DJ. Mobility as a service and private car use: Evidence from the Sydney MaaS trial. Transportation Research Part A: Policy and Practice. 2021;145: 17-33. doi: 10.1016/j.tra.2020.12.015.
  15. [15] Matowicki M, et al. Understanding the potential of MaaS–An European survey on attitudes. Travel Behaviour and Society. 2022;27: 204-15. doi: 10.1016/j.tbs.2022.01.009.
  16. [16] The mobility platform of the future. https://smartcity.wien.gv.at/en/smile-2/ [Accessed 4th Mar. 2022].
  17. [17] Junior W, Silva B, Dias K. A systematic mapping study on mobility mechanisms for cloud service provisioning in mobile cloud ecosystems. Computers & Electrical Engineering. 2018;69: 256-73. doi: 10.1016/j.compeleceng.2018.01.030.
  18. [18] Georgakis P, et al. Heuristic-based journey planner for Mobility as a Service (MaaS). Sustainability. 2020;12(23): 10140. doi: 10.3390/su122310140.
  19. [19] Maretić B, Abramović B. The spatial reorganization of an integration transport point: A case study of the city of Šibenik. Transport Problems. 2021;16(4). doi: 10.21307/tp-2021-056.
  20. [20] Pethő Z, Török Á, Szalay Z. A survey of new orientations in the field of vehicular cybersecurity, applying artificial intelligence based methods. Transactions on Emerging Telecommunications Technologies. 2021;32(1). doi: 10.1002/ett.4325.
  21. [21] Profillidis VA, Botzoris GN. Statistical methods for transport demand modeling. In: Modeling of Transport Demand. 2019. p. 163-224. doi: 10.1016/B978-0-12-811513-8.00005-4.
  22. [22] Ágoston G, Madleňák R. Road safety macro assessment model: Case study for Hungary. Periodica Polytechnica Transportation Engineering. 2021;49(1): 89-92. doi: 10.3311/PPtr.13083.
  23. [23] Vilke S, Mance D, Debelić B, Maslarić M. Correlation between freight transport industry and economic growth–panel analysis of CEE countries. Promet – Traffic&Transportation. 2021;33(4): 517-26. doi: 10.7307/ptt.v33i4.3688.
  24. [24] Zöldy M. Investigation of correlation between Diesel fuel cold operability and standardized cold flow properties. Periodica Polytechnica Transportation Engineering. 2021;49(2): 120-5. doi: 10.3311/PPtr.14148.
  25. [25] Peng Y, Xiaohe L, Jianbo S. A multi-attribute group decision making method considering both the correlation coefficient and hesitancy degrees under interval-valued intuitionistic fuzzy environment. Applied Soft Computing. 2021;104: 107187. doi: 10.1016/j.asoc.2021.107187.
  26. [26] Szabó Z, Török Á, Sipos T. Order of the cities: Usage as a transportation economic parameter. Periodica Polytechnica Transportation Engineering. 2021;49(2): 164-9. doi: 10.3311/PPtr.13786.
  27. [27] Štefančić G, Šarić S, Spudić R. Correlation between land use and urban public transport: Case study of Zagreb. Promet – Traffic&Transportation. 2014;26(2): 179-84. doi: 10.7307/ptt.v26i2.1471.
  28. [28] Forthofer RN, Lee ES, Hernandez M. Descriptive methods. In: Biostatistics. 2007. p. 21-69. doi:10.1016/b978-0-12-369492-8.50008-x.
Show more

SPECIAL ISSUE CALL

Innovation and New Technologies in Transport and Logistics

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Marko Matulin, PhD; Dario Babić, PhD; Marko Ševrović, PhD.

RECENT PAPERS

Browse through the selection of our recent research
Implications of Retrograde Behaviours on Visual and Cycling Behaviour of Normal CyclistsA Deep Learning Approach for Enhanced Real-Time Prediction of Winter Road Surface Temperatures in High-Altitude Mountain AreasOptimising the Economic Feasibility of High-Speed Maglev Systems: A Simulation-Based Approach for Variable and Parameter AnalysisVehicle Trajectory Prediction Based on GAT and LSTM Networks in Urban Environments

Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal