Modeling Parking Behavior Under Uncertainty: A Static Game Theoretic versus a Sequential Neo-additive Capacity Modeling Approach

This paper proposes two types of parking choice models, a static game theoretic model and a dynamic neo-additive capacity model, to capture the competition among drivers for limited desirable parking spaces. The static game assumes that drives make decisions simultaneously and with perfect knowledge about the characteristics of the parking system and the strategies of their fellow drivers in the system; the model thus captures only the rational aspect of parking choice behavior and pays no attention to modeling individual drivers’ psychological characteristics. The dynamic model, on the other hand, considers individual drivers’ psychological characteristics under uncertainty (i.e. optimistic and pessimistic attitudes) and thus captures the impacts of the irrational side of parking behavior in addition to the rational aspect. Following the formulation of the two models, they are both used to predict parking behavior as observed on a set of parking lots on the University at Buffalo north campus. Specifically for the dynamic model, the model is first calibrated based on real data collected from video recorded observations for a pair of parking lots, and then used to predict behavior on another pair. Validation results show higher predictive accuracy for the dynamic neo-additive capacity model compared to the static game theoretic model. This in turn suggests that the psychological characteristics of drivers play an important role in the parking lot choice decision process, and points to the potential for parking information systems to eliminate the unnecessary additional traffic generated by the parking search process. Copyright Springer Science+Business Media New York 2013