Eco-driving is a multidimensional concept that includes driving behavior, route selection and all other choices or behaviors related to the vehicles’ fuel consumption (e.g., the use of quality fuel, the use of air conditioning, driving at peak hours, etc.). The scope of this paper is to present an overview of recent literature referring to eco-driving and developed models for calculating fuel consumption, as well as the most important factors affecting it. Recent literature contains a large number of models that estimate fuel consumption, based on naturalistic driving data, which are collected using smartphones and OBDs. In this work, the existing literature is critically assessed in relation to conceptual, methodological and data related aspects. The analyses result to a set of limitations and challenges that are further discussed in the framework of system wide implementations for deriving policies that increase drivers’ awareness, but also improve system performance.

This paper examines the parameters that can lead to the acceptance of smartphone gamified applications concerning the airport. A questionnaire survey was conducted, where travelers were asked to express their needs and requirements, when accessing the airport or using its facilities. Logistic regression methodology is used to analyze the acceptance of such applications and the willingness to pay for them. In addition, a multivariate probit model is developed to investigate the factors that affect the frequent use of the application. Economic rewards appear to have a significant impact on the users' acceptability towards using a smartphone application for airports. Findings from the statistical modelling show that people who travel rarely and those who would like to have assistance when at the airport are more likely to use such an application. Furthermore, indoor navigation and information about the airport's services are important features of the application and the willingness to pay.

The evolution of Connected, Cooperative, and Automated Mobility (CCAM) systems represents a shift in transportation, potentially achieving benefits in efficiency, sustainability, and safety. Large-scale deployments of CCAM systems are, however, still constrained by fragmented Operational Design Domain (ODD) and limited infrastructure readiness. This paper reviews the state of the art regarding operational, infrastructural, and technological enablers for predictive and extendable ODDs. First, a literature review of existing definitions and ongoing standardization work is presented, focusing on gaps in the formalization and validation of ODD boundaries. Second, the influence of physical infrastructure elements on vehicle performance and safety is analyzed. Third, technological and organizational enablers, which include digital twins, data-driven simulation models, and governance frameworks, are discussed in depth as essential in adaptive and resilient CCAM operations. The review concludes that predictive and extendable ODDs require a data-driven and interoperable mobility ecosystem linking vehicles, infrastructure, and governance. Future research should focus on developing measurable indicators for infrastructure readiness, advancing simulation tools for dynamic ODD monitoring, and integrating human-in-the-loop systems for safe mixed traffic. Aligning these advances with Safe System Design and AI governance frameworks will enable scalable and trustworthy automated mobility.