maximum turnover in shopping areas to providing maximum parking convenience for local citizens. These multidimensional and often con- tradictory goals vary between cities and within cities and often remain implicit and cannot be combined into a set of predefined criteria. The goal of this research project is not to propose parking policies, but to develop a tool that enables the systematic analysis of the impacts of various policy scenarios using a set of quantifiable data relevant to policy makers. In the current paper, the tool will be used to assess a parking policy scenario in which additional parking supply is pro- vided to residents in a neighborhood with a parking demand–supply ratio well above one. Surprisingly, models have played a limited role in the analysis of urban parking policy and practices, with some notable exceptions [e.g., Shiftan and Golani (1) and Dell’Orco and Teodorovic ´ (2)]. Much of the modeling literature is theoretical in nature and has not been applied to real-world situations [e.g., Voith (3), Petiot (4), and Lam et al. (5)]. Much of the policy-oriented work, in turn, hardly makes use of the potential offered by state-of-the-art modeling tech- niques [see, for instance, Ferguson (6) and Marsden (7 ); the city is presently updating its comprehensive plan, maps, and appendices; new drafts are not yet available.)]. Against this background, it is pro- posed that an agent-based model (8) be used to simulate urban park- ing policy scenarios and analyze their impacts from a user and public policy perspective. BRIEF REVIEW OF PARKING MODELS Various types of models have been developed to simulate and ana- lyze drivers’ parking behavior in urban settings [for an elaborate review, see Young et al. (9) and Young (10)]. One side of the pole represents spatially implicit parking models. This includes the first generation of parking models developed from the late 1980s until today, based on studies of drivers’ stated preferences [e.g., Shiftan and Golani (1) and Axhausen and Polak (11)]. These and later models of the kind are static in nature and assess drivers’ stated preferences with logit regression to explain and predict drivers’ choice of parking type or parking spot. A parallel stream of spatially implicit and aggre- gate, but dynamic, models is associated mostly with the economic view of parking processes [e.g., Arnott and Rowse (12), Arnott (13), Shoup (14), and Verhoef et al. (15)]. These models tend to formulate an empirically testable pair—parking conditions and parking policy— that optimizes parking utilization per se, peak-hour traffic flows, departure time, or other key parameters (2, 4, 13, 16–19). The other type of parking model—of spatially explicit simula- tions of drivers’ parking search and choice behavior—started in the 1990s and is still in its infancy. The first attempts in this direction deal Evaluating Urban Parking Policies with Agent-Based Model of Driver Parking Behavior Karel Martens and Itzhak Benenson 37 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 infra- structure 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 des- tinations. 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 enforce- ment 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. Parking policies have a strong impact on the functioning of cities. The introduction of a new parking policy or changes in the existing policy—for example, differentiation of prices, limitations in park- ing time, or the establishment of prohibited areas—require a careful analysis and evaluation of these impacts in light of policy goals. What is a good parking policy? The answer depends on the ambi- tions of politicians and citizens concerning their city, constraints imposed by the urban physical environment, and the demand for park- ing. The goals of the policy can vary enormously, ranging from guar- anteeing optimal accessibility for car users to minimizing car use in the city, from safeguarding optimal traffic flow to limiting nuisance from (legally and illegally) parked cars, and from creating conditions for K. Martens, Institute for Management Research, Radboud University Nijmegen, P.O. Box 9108, 6500 HK, Nijmegen, Netherlands. I. Benenson, Department of Geography and Human Environment, Tel Aviv University, Ramat Aviv, 69978 Tel Aviv, Israel. Corresponding author: K. Martens, k.martens@fm.ru.nl. Transportation Research Record: Journal of the Transportation Research Board, No. 2046, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 37– 44. DOI: 10.3141/2046-05