Highlights We employ a serious game to reveal parking search behavior. AFT and MNL regression models are applied to analyze parking choices. Driver behavior is suboptimal, myopic and risk-averse. Drivers’ search path choice complies with a biased random walk model. Formalized decision making rules can be used to characterize agents in a parking ABM.
Abstract Drivers cruising for scarce on-street parking in city centers create negative externalities, including congestion and pollution. We apply a serious game – PARKGAME – to understand and model drivers' two intertwined instantaneous parking choices: when to quit cruising and where to cruise. Forty-nine participants took part in a lab experiment in which they had to arrive on time to a fictional appointment or face monetary penalties, and had to choose between uncertain but cheap on-street parking or a certain but costly parking lot. Scenarios diverged on the time to appointment and distance between the meeting place and parking lot locations. Participants played a series of 8 or 16 computer games on a Manhattan grid road network with high on-street parking occupancy and a nearby parking lot of unlimited capacity. Players’ parking choices were analyzed with accelerated-failure time (AFT) and multinomial logistic regression models. Results show that drivers are mostly myopic and risk-averse, and quit their on-street parking search long before the optimal moment. Spatially, drivers are attracted by the lot-destination axis, and their turn choices at junctions comply with a second-order biased random walk. The implications of game-based methods for simulation model development and sustainable parking policy are further discussed.
Modeling parking search behavior in the city center: A game-based approach
2020-09-12
Article (Journal)
Electronic Resource
English
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