Effects of Noisy Occupancy Data on an Auction-based Intelligent Parking Assignment

Abstract

Smartphones and cloud services can provide sophisticated parking assignment in modern intelligent cities. These solutions aim to guide drivers to vacant parking lots near their destination, reducing the necessary cruising for parking. Hence, they can smoothen the traffic flow and mitigate harmful emissions. Moreover, auction-based assignment can also dynamically optimize the actual parking prices, benefiting drivers, and parking lot operators. To operate such a system, we shall know the actual occupancy of the supervised parking lots. This data can come from various sources, e.g., crowdsourcing, parking lot operators, or third-party data providers. Sensing and fusing these records might lead to inaccurate input for the assignment method. In this paper, we analyze the impact of such noise on the performance of an auction-based parking lot assignment system. The results indicate that accurate information is crucial for perfect operation, but current state-of-the-art solutions provide sufficient input to benefit from the system.