Evaluating urban parking policies with agent-based model of driver parking behavior

This paper presents an explicit agent-based model of parking search in a city. In the model, "drivers" drive toward their destination, search for parking, park, remain at the parking place, and leave. The city's infrastructure is represented by a high-resolution geographic information system (GIS) of the street network and parking lots; information is included on traffic directions and permitted turns, on-street parking permissions, and layers of off-street parking places and lots. Destinations are presented by layers of dwellings and public places. Driver agents belong to one of four categories: residents and guests with dwellings as destinations and employees and customers with public places as destinations. Each agent has its own destination, willingness to pay, time of arrival, and duration of stay. In the model, driver agents are "landed" at a distance of approximately 250 m from their destination, that is, close to the area in which drivers start searching for parking. First, a driver estimates the parking situation in the area and then starts to search for a parking place. During the search, a driver agent accounts for the availability of parking places, differences in pricing, and parking enforcement efforts. The model outputs include distributions of (a) search time, (b) distance between parking place and destination, (c) fees paid by the drivers, and (d) parking revenues for the proprietor of paid parking places (whether local authority or private operator). The model is implemented as an ArcGIS application and applied to analyze parking dynamics in an inner city neighborhood in Tel Aviv, Israel, during the course of a regular weekday.