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.@en

Safety evaluation is a critical aspect through the future stages of automation development. Since there is a lack of historical and generalizable safety data in high levels of Connected and Autonomous Vehicles (CAVs), a possible approach to follow is the microscopic simulation method. Through microsimulation, vehicle trajectories are able to be exported and traffic conflicts to be identified using the Surrogate Safety Assessment Model (SSAM). Therefore, it is crucial to develop techniques in order to analyze conflict data extracted from microsimulation and evaluate crash data aiming to support road safety applications of automation technologies. This paper attempts to propose a safety evaluation approach for estimating crash rate of CAVs through microsimulation. For this purpose, the city center of Athens (Greece) was modelled using the Aimsun Next software paying attention to the calibration and validation of the model using real data of traffic characteristics. Moreover, different scenarios were formulated concerning different market penetration rates (MPRs) of CAVs and two fully automated generations (1st and 2nd generation) were simulated for modelling them. Subsequently, the SSAM software was used in order traffic conflicts to be identified and then converted to crash rate. Analysis of the outputs along with traffic data and network geometry characteristics were then conducted. The results indicated that in higher CAV MPRs, crash rates will be significantly lower as well as when the following-vehicle in the occurred conflict is a 2nd generation CAV. Lane change conflicts caused the highest crash rates compared to rear-end conflicts, which presented the lowest rates.@en