MaaS for commuting trips

MaaS for commuting trips: Potential for success or deception?

Knijn, Robin (TU Delft Civil Engineering and Geosciences)

Molin, E.J.E. (mentor)
van Wee, G.P. (mentor)
van Oort, N. (mentor)
Scheltes, Arthur (mentor)

Delft University of Technology

Transport, Infrastructure and Logistics

2020-05-08

The promises of Mobility-as-a-Service (MaaS) are generous, researchers and policymakers repeatedly present MaaS as a Holy Grail. MaaS has the potential to play a role in addressing issues as pollution, congestion and crowded cities. However, without interested users, these promises cannot be fulfilled. This study explores whether the proposed expectations of MaaS are realistic by examining the interest of potential users in MaaS. The focus is on employees in the Netherlands. Commuting trips might make an interesting first market for MaaS since there are possibilities to increase the interest of employees by financing commuting MaaS trips by employers. A latent class cluster analysis is performed to identify groups of employees based on the attitude towards and the intention to use MaaS for commuting trips. The results indicate that the majority of employees are not interested in using MaaS to commute. An increase in interest is seen when employers finance MaaS. However, this increase is not substantial. The interested groups of employees are individuals with high environmental consciousness and low car affection which often travel in a multi-modal way. This research concludes that the benefits of MaaS that are proposed by researchers and policymakers are overestimated. However, this does not imply that the MaaS concept should be discarded. The MaaS platform might be of added value for some travellers since it provides new travel options in case of unexpected disruptions and an easy and clear way of booking and paying for a door-to-door trip. For now, it is important to present MaaS in a more realistic way instead of the Holy Grail.

Mobility as a Service
Latent Class Cluster Analysis
Commuting mode choice
Factor Analysis

http://resolver.tudelft.nl/uuid:1617704b-200a-4ae2-bc12-46163bdd556b

Student theses

master thesis

© 2020 Robin Knijn