Bounded rationality can make parking search more efficient: The power of lexicographic heuristics

Abstract

The search for parking space in busy urban districts is one of those routine human activities that are expected to benefit from the widespread adoption of pervasive sensing and radio communication technologies. Proposed parking assistance solutions combine sensors, either as part of fixed infrastructure or onboard vehicles, wireless networking technologies and mobile social applications running on smartphones to collect, share and present to drivers real-time information about parking demand and availability. 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, and in sharp contrast with the existing literature, we model drivers as agents of bounded rationality and assume that their choices are directed by lexicographic heuristics, an instance of the fast and frugal heuristics developed in behavioral sciences such as psychology and biology. We analyze the performance of the search process under these heuristics and compare it against the predictions of normative game-theoretic models that assume fully rational strategically acting agents. We derive conditions under which the game-theoretic norms turn out to be more pessimistic than the simpler heuristic choice rules and show that these are fulfilled for a broad range of scenarios concerning the fees charged for the parking resources and their distance from the destinations of the drivers’ trips. The practical implications of these results for parking assistance solutions are identified and thoroughly discussed.