Decisional processing on parking behavior in entropic settings

This paper surveys the most recent advances in the context of decisional processing with focusing on the parking behavior in entropic settings, including the measures and the necessary mechanisms for the interaction of the actors-players, and their connection to decisional processing theory. The aim of this article is to provide a critical review of the most fashionable models and methods in parking lot financial design: the first class of methods covers the approach of analysis with the random entropic model; the second class of methods is the decisional processing through rational choice models as rational individual evaluations. Both techniques are described in detail in sections; we illustrate them using the well-known and easy multimodal problem approach and then we present the advanced applications. Thus, it is possible to identify all strong and weak points of the models and to compare them for a best feasible solution for parking lot economic and financial design. Taking into account a close equivalence between the aggregate methods of entropy maximization and disaggregated microeconomic method of discrete choice models, based on random utility theory, we try to provide a critical approach of it through the rational choice models and to underline the possible benefit of it for the problem decision.