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Recent technological advances in vehicle automation and connectivity have furthered the development of a wide range of innovative mobility concepts such as autonomous driving, on-demand services and electric mobility. Our study aimed at investigating the interplay of these concepts to efficiently reduce vehicle counts in urban environments, thereby reducing congestion levels and creating new public spaces to promote the quality of live in urban cities. For analysis, we implemented the aforementioned factors by introducing the concept of robo-taxis as an autonomous and shared mobility service. Using SUMO as the simulation framework, custom functionalities such as ride sharing, autonomous driving and advanced data processing were implemented as python methods via, and around, the TraCI interface. A passenger origin-destination matrix for our region of interest in Milan was derived from publically available mobile phone usage data and used for route input. Key evaluation parameters were the density-flow relationship, particulate-matter emissions, and person waitingtimes. Based on these parameters, the critical transition rate from private cars to robotaxis to reach a free-flow state was calculated. Our simulations show, that a transition rate of about 50% is required to achieve a significant reduction of traffic congestion levels in peak hours as indicated by mean travel times and vehicle flux. Assuming peakshaving, e.g. through dynamic pricing promised by digitalization, of about 10%, the threshold transition rate drops to 30%. Based on these findings, we propose that introducing a robo-taxi fleet of 9500 vehicles, centered around mid-size 6 seaters, can solve traffic congestion and emission problems in Milan.

Simulating the Impact of Shared, Autonomous Vehicles on Urban Mobility – a Case Study of Milan