The increasing availability and adoption of shared vehicles as an alternativeto personally-owned cars presents ample opportunities for achieving moreefficient transportation in cities. With private cars spending on the averageover 95% of the time parked, one of the possible benefits of shared mobilityis the reduced need for parking space. While widely discussed, a systematicquantification of these benefits as a function of mobility demand and sharingmodels is still mostly lacking in the literature. As a first step in thisdirection, this paper focuses on a type of private mobility which, althoughspecific, is a major contributor to traffic congestion and parking needs,namely, home-work commuting. We develop a data-driven methodology forestimating commuter parking needs in different shared mobility models,including a model where self-driving vehicles are used to partially compensateflow imbalance typical of commuting, and further reduce parking infrastructureat the expense of increased traveled kilometers. We consider the city ofSingapore as a case study, and produce very encouraging results showing thatthe gradual transition to shared mobility models will bring tangible reductionsin parking infrastructure. In the future-looking, self-driving vehiclescenario, our analysis suggests that up to 50% reduction in parking needs canbe achieved at the expense of increasing total traveled kilometers of less than2%.
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