<p>In this paper, we begin by modeling the parking garage revenue management problem as a deterministic network revenue management system with fixed capacity. For a given hourly pricing structure, our model uses a network linear programming approach, incorporating factors such as arrival time, departure time, and length of stay to maximize revenue. We assume that vehicle demand increases during the day, peaks at midday, fluctuates in the evening, and declines later. In many practical problems, obtaining data on the hourly length of stay (LOS) is often difficult, especially in countries where parking revenue management is underdeveloped. However, data on average hourly cars arrivals are available, enabling vehicle demand modeling using a nonhomogeneous Poisson process with variable mean arrival rates. Finally, we study how revenue changes with demand fluctuations. We assume a fixed length of stay distribution throughout this study. By adjusting demand, we observe that the system operates below capacity early in the day, reaching full capacity as arrival rates increase. When capacity reaches its limit, the service provider can increase prices to regulate demand. Therefore, if car arrival rates are predictable, the model reveals opportunities to charge higher prices as the facility approaches full capacity. Using this model, we derive bid or shadow prices and examine how these prices fluctuate in response to car arrival rates at the parking facility.</p>