Connected, Cooperative and Automated Mobility (CCAM) are expected to be introduced in increasing numbers over the next decade based on the rapidly developing capability of modern technologies. The need for policies around the introduction of CCAM is starting to arise, based on the evaluation of the likely impact of different technologies, with the aim to capture the benefits of automation and ensure that new technologies contribute to wider policy objectives. The Horizon 2020 Levitate project aims to investigate the potential short, medium and long term impacts of CCAM, through an innovative multi-disciplinary impact assessment methodology, which will be incorporated within a new web-based policy support tool to enable city and other authorities to forecast impacts of CCAM on urban areas. The aim of the present paper is to provide an insight on the development of the Levitate Policy Support Tool (PST), the use cases, parameters and impacts considered and the methodologies applied for the estimation of relationships and impacts of connected and automated transport systems. This policy support tool will comprise a knowledge and an estimator module and will include forecasting and backcasting systems providing estimates for different types of impacts and allowing comparative analyses. The Policy Support Tool will integrate the methodologies and findings of the Levitate project, in order to develop an overall framework for the assessment of impacts, benefits and costs of CCAM for different automation and penetration levels and on different time horizons, as well as a public toolkit and a decision support system allowing the testing of various policy scenarios on the basis of the needs of relevant stakeholders.

The aim of this paper is to extend past research on travel behavior analysis by investigating travelers’ emotions and perceptions of the system’s performance. Perceived travel happiness as an extension of travel satisfaction is researched in the framework of the decision-making process during traveling. Socio-demographic, cognitive, and affective data were collected from a questionnaire survey that took place in Athens (Greece), the Netherlands, and Barcelona and Salamanca (Spain). A Bayesian network was developed to investigate the interrelations between travel happiness and parameters that affect travel behavior. Findings revealed that travel mode choice directly affects the level of happiness that a person experiences during everyday trips. Moreover, travel happiness is directly associated with the traveler’s perception of the occurrence of disruptions during everyday trips and the level of tolerance he/she has toward such disruptions. Results also indicated that further research should focus on understanding how the topology and performance of each country’s system affect travel-related choices. Finally, a discussion of the most significant results is provided.