A Quality of Service-Aware Least Utilization First Algorithm for Smart On-Street Parking Management

Abstract

The rapid growth in the number of vehicles and limited parking slots has made on-street parking management an increasingly complex challenge. Intelligent transportation systems (ITS) have emerged as a key solution, enhancing the efficiency of smart on-street parking management by delivering real-time information on slot availability, optimizing space utilization, and significantly reducing the time drivers spend searching for parking. Researchers have developed various pricing strategies to map the parking requests to the parking slots, maximizing revenue generation. However, most strategies only consider the provider’s perspective to improve the utilization of parking slots without considering the user’s perspective, leading to dissatisfaction among the users. Therefore, we model the user’s perspective using the quality of service (QoS) and introduce a QoS-aware least utilization first (LUF) algorithm for smart on-street parking management. LUF aims to maximize revenue generation through a dynamic pricing strategy without compromising user satisfaction. It satisfies the QoS of the parking requests submitted by the users, thereby selecting the less-utilized parking slots. The performance of LUF is evaluated by taking the Seattle city dataset and compared using three algorithms, namely first come, first serve (FCFS), round robin (RR), and highest price first (HPF), in terms of number of accepted requests (Racc), number of rejected requests (Rrej ), average price (AV) and revenue (RV). The simulation results across ten areas of the Seattle city dataset show that LUF outperforms in al the performance metrics compared to FCFS, RR, and HPF.