# Model for Determining Parking Demand Using Simulation-Based Pricing
## 1 Introduction
A review in [13] highlights research gaps and provides recommendations. Most existing models focus on spatial demand distribution, with temporal aspects often underrepresented, typically limited to adjustments in payment start times or parking durations to reduce policy restrictiveness [28]. To enhance policymaking, it is essential to assess the influence of prices on user composition across different times of day, enabling sustainable parking management based on revealed preferences. Our research presents a model that determines optimal parking prices based on garage occupancy and user composition. Two criteria guide the price simulations: first, prices must not result in forecasted occupancy above 95%; second, they must generate higher revenue than current prices. The first criterion ensures sufficient available spaces number (hypothesis 1), while the second supports economic sustainability. The model contributes to more effective parking and urban transport policies, thereby enhancing the overall quality of urban life. The foundational economic models of parking elasticity [19; 20] provide important theoretical insights but generally assume constant demand across user categories and time periods. In contrast, our model adopts a predictive and scenario-based approach. Unlike [22], which implemented iterative block-level price adjustments based on occupancy data without differentiating user types or analyzing how pricing affects user composition, our model explicitly supports user-class-specific demand simulation. This capability enables more detailed analysis of price impacts on distinct user categories and supports the development of targeted parking policy interventions. Similarly, current machine-learning models used for parking demand forecasting achieve high predictive accuracy at the level of aggregate occupancy, but they generally do not support policy simulations or modeling of user-class-specific scenarios [29]. Our simulation-based framework addresses this gap by enabling user-class-specific pricing optimization and providing a practical tool for operational policy evaluation based on revealed preference data. Urban traffic congestion is significantly exacerbated by drivers cruising in search of available parking spaces. Studies report that up to 30% of urban traffic congestion in OECD countries can be attributed to vehicles engaged in parking search circulation. This behavior generates substantial air pollution and fuel consumption, contributing to environmental harm through elevated greenhouse gas emissions, and imposing social costs through lost productivity. The cumulative effect of billions of vehicle kilometers driven in search of parking results in considerable time loss and economic inefficiency [30]. To address these challenges, this study introduces a simulation-based pricing model that seeks to optimize garage occupancy, reduce cruising traffic, and mitigate the associated environmental and social impacts of parking-related congestion. While much of the earlier literature has focused on demand elasticity and static pricing strategies, recent developments in parking management increasingly emphasize simulation-based demand forecasting and the application of phase transition theory to traffic systems. Simulation-based tools offer robust methods for forecasting complex demand responses to varying pricing schemes, thereby supporting more effective decision-making in parking management. Phase transition theory is particularly relevant in this context, as it explains how traffic systems transition from free-flow conditions to congested states, especially when parking availability is constrained. Conceptual frameworks such as the congestion boundary approach [31] and the lattice hydrodynamic model [32] provide valuable methodologies for modeling, calibrating, and validating these dynamics, and contribute foundational insights for managing critical transitions in parking demand and urban traffic congestion.