Estimating traffic conflict severity for Connected and Automated vehicles using simulation-based surrogate safety indicators Miqdady, Tasneem Oña López, Rocío de Oña López, Juan José De Connected and Autonomous Vehicles (CAV) is the developing summit of the integration between artificial intelligence (AI), robotics, automotive design and information technologies. Many researchers are investigating their effects on traffic safety. This study tries to quantify the volume of incidents when sharing the road human-driven vehicles and fully CAV. After modeling the geometry of 4.5 km of motorway and the parameters of connectivity and automation using Aimsun Next platform, several scenarios of the percentages of CAV (0%, 25%, 50%, 75%, and 100%) were driven in microsimulation runs. Then the microsimulation generated vehicles trajectories that are used to identify conflicts using the Surrogate Safety Assessment Model (SSAM). The results of this analysis confirm previous research in that the reduction of number of conflicts will be up to 35% with low and moderate penetration rates of CAV and more than 80% if the road is operated only with CAV. 2024-07-19T09:32:53Z 2024-07-19T09:32:53Z 2021 conference output T. Miqdady, R.de Oña and J. de Oña (2021). Estimating traffic conflict severity for Connected and Automated vehicles using simulation-based surrogate safety indicators. 20º Congreso Chileno de Ingeniería de Transporte (CCHIT 20), 2021 https://hdl.handle.net/10481/93255 open access