Fighting for curb space: Micro-simulation

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.本研究针对路侧空间管理相关的多个主题开展了全面的文献综述，探讨了各类路侧使用群体（如行人、自行车使用者、公共交通车辆、出租车及商用货运车辆），总结了不同实践经验，并将讨论重点聚焦于完整街道（Complete Street）策略。此外，作者团队梳理了关于路侧与停车数据采集、仿真及优化技术的学术文献。以旧金山中心城区周边区域为案例研究对象，作者团队针对多种停车行为场景评估了该系统的运行性能。作者团队基于一系列停车行为模式（如停车寻位、可获取路外停车信息的停车行为、双排停车），在SUMO中开发了停车仿真模型。这些场景在三个基于土地利用类型划分的子研究区域中完成测试，分别代表居住区、商业区及混合功能区。本数据集包含三个研究区域的地理信息系统（GIS）信息以及SUMO仿真脚本。