Application of crowdsourced data to infer user satisfaction with Mobility as a Service (MaaS)

Mobility as a Service (MaaS) is a user-centric concept that integrates transportation services, and offers a multimodal journey planner, and online booking and payment options into a single digital platform. Achieving the goal of MaaS, which is to promote sustainable forms of transportation and reduce single occupancy vehicle travel, depends on the needs of different user groups. A review of literature confirms that little remains known about gender-based perspectives towards MaaS. This study aims to identify the features that impact satisfaction across gender. After collecting over 29,000 online reviews, series of text mining and unsupervised machine learning technique were performed to extract features discussed by users in the reviews. Next, ordinal logit regression was used to examine associations between extracted features and level of satisfaction. Findings show that public transit is still the backbone of MaaS, as such, other modes of transportation such as micromobility and ridesharing still receive little attention from users. Although, integrated payment options and booking systems were meant to offer MaaS an advantage over classic journey planners, still fare related features were found to contribute to user dissatisfaction. App related features (such as map, technical issues, and updates), often neglected in transportation studies, showed significant association with satisfaction. One of the main contributions of this study is the expanded understanding of gendered attitudes towards MaaS. Few studies have explored gender perspectives towards new mobility concepts, and particularly MaaS. Increased understanding of gendered perspectives encourages the use of MaaS, and ultimately enhances travel experiences for all users.