Macroscopic on-street parking inventory modeling: Exploring an open data approach

Parking plays a vital role in shaping land use and transport. Despite occupying significant portions of urban space, detailed data on parking locations and capacities are often unavailable. Recognizing the critical significance of such data for comprehensive transportation modeling and sustainable urban planning, this study presents two statistical models designed to predict the available on-street parking length in urban traffic analysis zones. The first model uses OpenStreetMap (OSM) data as its primary input, while the second is based on official parking inventory data from the city of Berlin. Both models are built using multiple linear regression, with land use and built environment characteristics as independent variables. The models are evaluated by applying them to the city of Munich. This research provides new insights into the spatial distribution of urban on-street parking and offers a practical approach for estimating parking supply to support sustainable urban development strategies.