Authors: Arhaan Shaikh
Abstract: Parking congestion has become one of the major challenges in modern urban areas because of rapid population growth, expansion of cities, and the continuously increasing number of private vehicles. In many commercial zones, res-idential complexes, shopping malls, railway stations, airports, and educational campuses, drivers often face difficulty in finding available parking spaces. This leads to unnecessary delays, traffic buildup, fuel wastage, driver frustration, and increased air pollution. Traditional parking management systems generally depend on fixed thresholds or simple binary decision-making methods, where congestion is classified only as full or empty. Such systems are not flexible enough to handle real-time changes in parking demand and uncertain traffic situations. This paper presents a Mamdani fuzzy logic-based smart parking congestion detection system that can intelligently es-timate parking congestion levels using two important input parameters: vehicle count and free slot availability. Instead of using rigid boundaries, fuzzy logic uses linguistic terms such as Low, Medium, and High to represent real-world conditions more naturally. The proposed model uses triangular membership functions for fuzzification, a nine-rule inference engine for decision-making, and centroid defuzzification to generate a final congestion output. The system provides smoother transitions between congestion states, better handling of boundary values, and more realistic results compared to conventional methods. Due to its low computational complexity, the proposed system is highly suitable for real-time embedded devices, IoT-based smart city applications, and automated parking guidance systems.
