SUMO for parking studies: Challenges and opportunities
summary
SUMO is used to generate parking data for low- and mid‑income city streets by importing an OSM‑derived network with manual parking inventory and adding lanes, then controlling vehicles via TraCI API tweaks to force rerouting, cruising speeds and dynamic demand. Simulations compare a uniform demand to a bimodal Gaussian demand (twice‑daily peaks) and show travel time rises and network average speed falls as parking demand increases; parking occupancy logs reveal many spaces remain underutilized. Main problems are manual encoding of unmarked spots, rigid vehicle parking logic, and lack of real data for validation.
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