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. 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.
The data contains the GIS information of the three study areas, and the SUMO scripts.
The team used public information about land uses and socio-demographic characteristics in the study area.
Trips were taken from the Metropolitan Transportation Council Activity Based Model in the Bay Area, and transferred to SUMO (and an intermediate step in MatSIM). For freight, the team implemented trip generation models for commercial establishments.