# Model for Determining Parking Demand Using Simulation-Based Pricing
## 5 Conclusions
The current parameters should therefore be regarded as initial estimates, capturing the direction and relative magnitude of price responsiveness, but lacking validation for reliable predictive performance across varied operational contexts. To improve the precision of parameter estimates and enhance statistical robustness, future research should incorporate multi-day observations across a range of facilities, pricing levels, and temporal conditions. Larger datasets will enable the calculation of confidence intervals for demand parameters, identification of potential non-linear effects and threshold responses to price adjustments, and the differentiation between structural demand patterns and isolated anomalies. Such refinement is essential to strengthen both the model's generalizability and its predictive accuracy. Independent or out-of-sample data has not yet been used to validate the model. This limitation stems from the restricted data scope, as elasticity parameters were calibrated using a single 24 h before-and-after observation period at one facility. Without validation on out-of-sample data, the model currently cannot provide formal measures of predictive accuracy or report evaluation metrics such as RMSE, MAE, or confidence intervals. As a result, the current parameter values should be interpreted as contextual indicators, rather than as statistically validated estimates. We acknowledge this as a primary limitation of the present study. However, we have already begun acquiring multi-day occupancy and pricing data from multiple facilities. The expanded dataset will support back-casting, statistical validation, and sensitivity analysis which will enhance the model's generalizability and predictive reliability. The current version of the simulation model treats hourly intervals as independent units. As a result, it does not capture temporal dependencies in user behavior--specifically, how parking decisions at a given time may be influenced by parking space availability in earlier hours. For example, users who encounter access denial during morning hours may choose not to attempt parking later in the day, resulting in temporal spillover effects that the model does not currently represent. Future development of the model will consider incorporating dynamic simulation techniques or agent-based modeling to better account for these cumulative behavioral effects. The model currently assumes that user categories remain fixed and independent and does not account for potential substitution effects between different user segments. For example, a reduction in CMP demand may cause some users to switch to STU services or to seek alternative local parking options. Future enhancements to the model may incorporate cross-elasticity functions and behavioral modeling techniques to capture such interdependencies and more accurately represent substitution dynamics across user categories. The present simulation study analyzes pricing as the sole adjustable policy variable. Other instruments--such as parking time limits, targeted discounts, digital reservation systems, and congestion-responsive pricing--also influence parking demand, but were excluded from this analysis due to data limitations. These factors represent important avenues for future model enhancements, and their incorporation will enable a more comprehensive representation of parking management strategies. The model currently assumes uniform behavior within each user category and does not account for individual-level variations such as walking distance tolerance, likelihood of repeat visits, or time-of-day preferences. Future versions of the model will seek to incorporate behavioral heterogeneity through the use of more detailed data sources, and by applying agent-based modeling or choice modeling frameworks. The simulation was conducted using data from a single public garage, which limits the model's ability to generalize to other contexts.