Geosimulation of Parking in the City

We present PARKAGENT, an agent-based, spatially explicit, model for parking in the city. PARKAGENT is based on the geosimulation approach, combining real-world GIS database with a multi-agent system. The model simulates the behavior of each driver in a spatially explicit environment and is able to capture the complex self-organizing dynamics of a large collective of parking agents within a non-homogeneous (road) space. The model is developed as an ARCGIS application and can work with a practically unlimited number of drivers. Standard model outputs include distributions of search time, walking distance, and parking costs, each of which can be generated per driver groups, per area and per time interval. Based on field estimations of supply of and demand for parking and parameters of drivers‟ behavior, we apply PARKAGENT for investigating several parking scenarios in an over-saturated situation in Tel Aviv. The model shows that, while a limited amount of additional parking has hardly any impact on average search time or walking distance, it strongly affects the occurrence of extreme values. We also compare the effects of a concentrated versus a spatially distributed addition of new parking facilities in a Tel Aviv neighborhood, where demand for parking essentially