Data, AI and governance in MaaS – Leading to sustainable mobility?

Servou, Eriketti; Behrendt, Frauke; Horst, Maja

doi:10.1016/j.trip.2023.100806

Cited by 3 publications

(2 citation statements)

References 85 publications

(162 reference statements)

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“…Concerning the problem of data justice, lot of investigations ignore inequalities the certain groups of people use digital devices less than others and there is a lack of data from specific user groups. This way, MaaS runs the risk to facilitate accessibility only for "digital" users and reproduce existing mobility inequalities and injustice in cities, if data collection relies only on digital means (Servou et al 2023).…”

Section: Identified Gaps and Challengesmentioning

confidence: 99%

“…It plays the crucial role for creating tailored and suitable environment for different types of users with different expectations and it is the keystone of personalization. The solution of mobility personalization issues has become possible in the era of big data and AI (Servou et al 2023;Diran et al 2021) and it provides new models and tools to identify personal patterns and customize the solutions on-the-go.…”

Section: Introductionmentioning

confidence: 99%

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Mobility-as-a-Service: Literature and Tools Review With a Focus on Personalization

Turno,

Yatskiv Jackiva

2023

Transport

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In the coming years, mobility initiatives should focus on sustainability, safety, and social equity. This can be achieved by introducing innovative transportation methods, implementing novel approaches for end-users, and optimizing the utilization of traditional modes of transport. To achieve this goal, it is essential to utilize pervasive sensing and computing technologies, along with intelligent information processing systems, to assist decision makers, managers, and transport operators. To effectively address unforeseen events and disruptions, mobility services should promptly adapt and improve their flexibility. Furthermore, these services should be adaptable to meet the unique needs and evolving demands of individuals. Current research focuses on understanding how individuals make decisions about when and where they engage in walking, driving, and travel activities. Therefore, it is important to develop reliable human mobility models in this context. Big data and Artificial Intelligence (AI) are important in this context as they enable data generators to identify individual patterns and quickly adapt solutions. This paper aims to conduct a literature review on Mobility-as-a-Service (MaaS), focusing on personalization, to identify gaps in current MaaS initiatives. This assessment is essential for creating inclusive, user-friendly, personalized, and customizable MaaS solutions. To conclude, the existing challenges have been addressed in comprehending the characteristics of MaaS in terms of personalization. Additionally, they have been proposed further research questions to delve deeper into this aspect.

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Section: Identified Gaps and Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%