A Game Theoretic Approach for Parking Spot Search with Limited Parking Lot Information
This addresses parking efficiency for drivers in structured lots, but it is incremental as it builds on existing strategies with a new computational method.
The paper tackles the problem of finding optimal parking spots in structured lots using limited layout and occupancy data, and demonstrates through simulations that their game-theoretic approach achieves lower cost function values compared to a state-of-the-art heuristic method.
We propose a game theoretic approach to address the problem of searching for available parking spots in a parking lot and picking the ``optimal'' one to park. The approach exploits limited information provided by the parking lot, i.e., its layout and the current number of cars in it. Considering the fact that such information is or can be easily made available for many structured parking lots, the proposed approach can be applicable without requiring major updates to existing parking facilities. For large parking lots, a sampling-based strategy is integrated with the proposed approach to overcome the associated computational challenge. The proposed approach is compared against a state-of-the-art heuristic-based parking spot search strategy in the literature through simulation studies and demonstrates its advantage in terms of achieving lower cost function values.