There is a need to optimally allocate curb space-one of the scarcest resources in urban areas-to the different and growing needs of passenger and freight transport. Although there are plenty of linear miles of curbside space in every city, the growing adoption of ride-hailing services and the rise of e-commerce with its residential deliveries, and the increased number of micro-mobility services, have increased pressure on the already saturated transportation system. Traditional curbside planning strategies have relied on land-use based demand estimates to allocate access priority to the curb (e.g., pedestrian and transit for residential areas, commercial vehicles for commercial and industrial zones). In some locales, new guidelines provide ideas on flexible curbside management, but lack the systems to gather and analyze the data, and optimally and dynamically allocate the space to the different users and needs. This study conducted a comprehensive literature review on several topics related to curb space management, discussing various users (e.g., pedestrians, bicycles, transit, taxis, and commercial freight vehicles), summarizing different experiences, and focusing the discussion on Complete Street strategies. Moreover, the authors reviewed the academic literature on curbside and parking data collection, and simulation and optimization techniques. Considering a case study around the downtown area in San Francisco, the authors evaluated the performance of the system with respect to a number of parking behavior scenarios. In doing so, the authors developed a parking simulation in SUMO following a set of parking behaviors (e.g., parking search, parking with off-street parking information availability, double-parking). These scenarios were tested in three different (land use-based) sub-study areas representing residential, commercial and mixed-use.
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