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

This study aims to quantify the safety impacts of Connected and Automated Vehicles (CAVs) in mixed traffic environments in three calibrated and validated urban road networks including Manchester (UK), Leicester (UK), and Athens (GR). Road safety impacts were investigated through traffic microsimulation techniques combined with application of the Surrogate Safety Assessment Model (SSAM). Behaviours of CAVs were modelled based on a comprehensive literature review and discussions with experts. The estimated number of conflicts, extracted from the microsimulation and SSAM approach, were converted to the number of crashes by using a probabilistic method. Results revealed a significant improvement in case of passenger car fleet scenarios in all three test networks. However, the mixed fleet scenarios involving freight and public transport vehicles showed added complexities due to non-homogeneity in vehicle characteristics. In this context, limitations of microsimulation and SSAM have also been identified while recommendations have been made for methodological improvements. Overall, the findings of this research provide several useful insights by using a practical procedure to estimate safety impacts under mixed traffic environment. Future research and field trials should focus on addressing the challenges of maintaining safety in the early and transition phases of the deployment of CAVs.@en