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Cluster Analysis of User Preferences related to MaaS Aspects
Rapid changes are happening in urban transportation, where new mobility services based on mobile applications are becoming popular. Mobility as a Service (MaaS) integrates different transport modes through a mobile application, which includes many aspects of traveling (e.g., routing, booking, ticketing, payment). Previous studies primarily focus on the potential adoption of MaaS, while users’ preferences for MaaS aspects are not well explored. Thus, the paper aims to examine the preferences toward various aspects of MaaS. This study applies a Latent Class Cluster Analysis (LCCA) to investigate the clusters of preferences for MaaS aspects. A binary logistic regression is performed to examine the relationship between the obtained classes, socio-demographic, and travel characteristics. The LCCA results in two clusters of the aspects, i.e., advanced, and traditional clusters. The developed model indicates that females, individuals with high income, and shared-mobility users are more likely to use the advanced aspects. Meanwhile, public transport usage and employment status factors do not have significant relationships with the clustering. This study could be beneficial for transport planners and MaaS operators in designing a suitable MaaS application based on the user preferences.
Cluster Analysis of User Preferences related to MaaS Aspects
Rapid changes are happening in urban transportation, where new mobility services based on mobile applications are becoming popular. Mobility as a Service (MaaS) integrates different transport modes through a mobile application, which includes many aspects of traveling (e.g., routing, booking, ticketing, payment). Previous studies primarily focus on the potential adoption of MaaS, while users’ preferences for MaaS aspects are not well explored. Thus, the paper aims to examine the preferences toward various aspects of MaaS. This study applies a Latent Class Cluster Analysis (LCCA) to investigate the clusters of preferences for MaaS aspects. A binary logistic regression is performed to examine the relationship between the obtained classes, socio-demographic, and travel characteristics. The LCCA results in two clusters of the aspects, i.e., advanced, and traditional clusters. The developed model indicates that females, individuals with high income, and shared-mobility users are more likely to use the advanced aspects. Meanwhile, public transport usage and employment status factors do not have significant relationships with the clustering. This study could be beneficial for transport planners and MaaS operators in designing a suitable MaaS application based on the user preferences.
Cluster Analysis of User Preferences related to MaaS Aspects
Kriswardhana, Willy (author) / Esztergar-Kiss, Domokos (author)
2023-06-14
286427 byte
Conference paper
Electronic Resource
English
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