Addressing the Minimum Fleet Problem for a Traditional and an Autonomous Car Sharing Service Based on Real Data

This study examines the transformative potential of autonomous vehicle (AV) car-sharing services for sustainable urban mobility, using Milan as a case study. Recent regulatory changes in Europe, aimed at reducing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C O_{2}$</tex> emissions and urban traffic congestion, have catalyzed the shift from internal combustion engines to electric vehicles (EVs). However, private car ownership and even current shared mobility models, such as freefloating car sharing, fall short of achieving both environmental impact and cost-effectiveness. Utilizing telematics big data of private cars as representative of real mobility demand, this research compares the efficiency of a free-floating sharing against an autonomous valet model. Results demonstrate a notable efficiency increase: while traditional car sharing reduces the fleet size by a quarter, the AV service cuts vehicle demand by up to 13 times, minimizing fleet size to meet urban demand with minimal waiting and walking time. This data-driven, simulation-based optimization underscores AV car sharing's potential to support urban sustainability goals by lowering vehicle number, curbing emissions, and enhancing service levels for users. The findings present a viable pathway toward the concept of Mobility as a Service (MaaS), which could redefine urban transportation by moving away from individual car ownership.