# Model for Determining Parking Demand Using Simulation-Based Pricing
## 5 Conclusions  
This study was motivated by an economically atypical situation in which an increase in parking prices paradoxically resulted in higher demand among certain user categories. This counter-intuitive outcome was observed during periods when total demand exceeded supply, leading to full occupancy of the parking facility. Under such circumstances, it was necessary to apply the chaotic demand model to determine the actual demand or to propose prices that satisfied the initial condition (4) and requirement (5). The central hypothesis of this research posited that, provided with enough available parking spaces, an increase in price would lead to a decrease in parking demand. Our analysis of aggregate data supported this hypothesis, as total demand declined in response to higher prices. However, a more detailed examination of individual user categories revealed that demand for some segments increased. This phenomenon was attributed to the prior shortage of available spaces during peak periods, which suppressed demand for these categories until the price adjustment was implemented. Following the price increase, overcapacity was eliminated, and higher-revenue user groups gained improved access to parking. Initial attempts to estimate gravitational demand parameters within the chaotic demand framework used the previously derived elasticity parameter \(\mu\) from Formula (15). Therefore, the model for optimal pricing required recalibration. The chaotic gravitational demand parameter K was subsequently adjusted using Formula (18) to align with the basic hypothesis (1). This modification produced gravitational demand values that aligned with observed outcomes and enabled the simulation model to generate valid demand forecasts. Once total demand was established, the model used simulated pricing to calculate demand while verifying the initial condition (4) and the revenue maximization requirement (5). During the research, the initial condition (4) was updated and now reads as condition (25). The analysis of demand changes and the factors influencing demand across different user categories was limited due to the infrequent price changes. Our model modifies user category demand through price simulation while meeting the updated initial model condition (25), and points to more profitable management in line with requirement (5). The model's structure is adaptable to a variety of parking environments, but it requires new demand parameter estimation for each specific application. Substantial variations in user composition and willingness to pay across different urban contexts necessitate local calibration based on observed responses to pricing adjustments. The model framework supports such calibration using relatively limited datasets, relying on basic before-and-after price comparisons to adjust key parameters to the local context. The simulation model highlights the fundamental trade-off between maximizing parking revenue and preserving user access through the management of occupancy limits. By identifying pricing strategies that maintain parking availability below 95% occupancy, the model offers a practical approach to simultaneously reduce traffic congestion and ensure financial sustainability. Policymakers can apply this method to develop dynamic pricing strategies tailored to time of day and user category, with the aim of achieving outcomes such as higher turnover rates, reduced emissions from cruising traffic, and more balanced utilization of parking facilities. While the model structure is adaptable, several limitations should be acknowledged. The model's key demand parameters were estimated from two observed data points per time slot--before and after a single price adjustment--collected during a single 24 h period at one public garage. This limited empirical foundation introduces the risk of overfitting to short-term fluctuations or context-specific anomalies.