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

Yinying He; Csaba Csiszár

doi:10.7307/ptt.v34i5.4126

Authors

Yinying He Budapest University of Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering
Csaba Csiszár Budapest University of Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering

DOI:

https://doi.org/10.7307/ptt.v34i5.4126

Keywords:

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

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.

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