LAUSR

A parking lot can accommodate a maximum number of vehicles, which determines its size. We model this parking lot as a family of Birth-Death Processes (BDPs) in equilibrium, with finite size, indexed by the parking lot utilization. Relying on the concepts of information and entropy (i.e., mean information) of a parking lot, we promote a methodology for revenue analysis of parking lots. Thereto, by definition, the park entropy is uncertainty, associated with the park. We evaluate the full park revenue (as a function of the full park information). Then, assuming the same Gaussian distribution for the revenue attained from any vehicle, the park revenue, its mean value and mean normalized square deviation of the park revenue from its linear part are also assessed. The last quantity is compared with park entropy, and park mean revenue with full park revenue, as functions of the park utilization, in order for an optimal trade-off between these measures to be established. Thus, when making decisions about the park utilization and size (as two key drivers of the parking business), park operators and planners who focus on the uncertainty become more proficient at their job than those who focus on revenue consequences only. The developed methodology is illustrated on the public parking lots, modeled as a family of BDPs with truncated Poisson distribution as its equilibrium one.