Optimizing parking prices using an agent based approach

Abstract

Many cities around the globe are haunted by limited supply of parking and inefficient fee schedules, which often result in additional traffic by people cruising for parking. Pricing parking in an optimal way both from the municipality’s and user’s perspective in order to balance demand and supply is essential to solve this problem. We propose an agent-based approach to iteratively find such an optimal parking price. A major innovation of our parking model is, that it takes taste heterogeneity of drivers into account. By applying the model to a test scenario in the city of Zurich, we successfully demonstrate, that the model provides key figures, which are essential for supporting decision of policy makers. The paper also discusses some future work, e.g. other applications of the model in the area of parking choice and parking search modeling.