ParkAssistant: An Algorithm for Guiding a Car to a Parking Spot

Parking search is a major issue in urban areas. Drivers in major cities face a daily struggle in finding parking space, and much of this is due to lack of information about parking rules, parking prices, traffic conditions, and parking availability. As a consequence, drivers often perform inefficient search for a parking space and spend too much time searching, pay too much, or park too far from an intended destination. Inefficient parking search is not a problem only for drivers, it is also increasing traffic congestion and pollution and it causes a distortion of the parking market. Despite the vast technological advances in recent decades, parking search remains fundamentally the same societal problem it has been for almost a century. The objective of this paper is to address this issue by proposing the ParkAssistant, an algorithm that calculates a cruising route that minimizes the expected cost of parking, defined as a mix of price and time to reach the destination. To calculate a good cruising route, the algorithm uses parking information that consists of parking rules, traffic conditions, probabilities of finding an empty parking space, and drivers’ utility function. The authors evaluated ParkAssistant through simulations using real-life parking occupancy data from San Francisco, CA. The results indicate that, as compared to an uninformed driver model, it allows drivers to find parking much faster. The results also show that the quality of ParkAssistant recommendations grows with the quality of parking information the method is provided with