Bounded rationality can increase parking search efficiency - Karaliopoulos, Merkourios S.; Katsikopoulos, Konstantinos V.; Lambrinos, Lambros (My University, 2014)
The search for parking space in busy urban districts is one of those routine human activities that can benefit from the widespread adoption of pervasive sensing and radio communication technologies. Proposed parking assistance solutions combine sensors, either as fixed infrastructure or onboard vehicles, wireless networking technologies and mobile social applications running over smartphones to collect, share and present to drivers real-time information about parking availability and demand. One question that arises is how does (and should) the driver actually use such information to take parking decisions, e.g., whether to search for on-street parking space or drive to a parking lot and, in the latter case, which one. The paper is, hence, a performance analysis study that seeks to capture the highly behavioral and heuristic dimension of drivers' decisions and its impact on the efficiency of the parking search process. To this end we model drivers as agents of bounded rationality and consider lexicographic heuristics, an instance of the fast and frugal heuristics developed in behavioral sciences such as psychology and biology, as the mechanisms for their decisions. We analyze the performance of the search process under these heuristics and compare it against the predictions of normative game-theoretic models assuming fully rational strategically acting agents. We derive conditions under which the simpler heuristic decision-making rules outperform the complex norms and show their satisfaction under a broad range of scenarios concerning the fees charged for the parking resources and their distances from drivers' destinations. The practical implications of these results for parking assistance solutions are identified and thoroughly discussed.
Keywords: Computing methodologies; Modeling and simulation; Model development and analysis; Modeling methodologies