Graph-Based Approach for Personalized Travel Recommendations

In the evolving domain of urban mobility systems, the integration of technology with user-centric strategies is pivotal. This research stands on the foundational concept of Mobility-as-a-Service, a user-centric intelligent mobility management distribution system that seeks to prioritize human needs over mere technological infrastructure. The study delves deep into the wealth of data available through mobile sensing technologies, highlighting the unprecedented understanding it offers into human mobility patterns, thus facilitating personalized route recommendations

Mobility-as-a-service: literature and tools review with a focus on personalization

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. First published online 26 February 202