Deep Learning-Driven Smart Parking Reservation System for Urban Traffic Management

Abstract

Parking scarcity and outdated availability data pose significant challenges in metropolitan areas, especially during peak hours. This study introduces an intelligent, reservation-based parking management system leveraging a deep learning framework that integrates Long Short-Term Memory and Deep Residual Recurrent Neural Networks (LSTM-DR-RNN). The proposed system enables real-time prediction and scheduling of vacant parking spots through a cloud-based mobile application, enhancing user convenience by considering regional parking conditions rather than isolated lots. By incorporating external factors such as weather conditions, calendar dates, and traffic flow, the model achieves improved forecasting accuracy. Evaluated using real-world datasets from multiple cities, the approach demonstrates reduced search time and fuel consumption. Comparative analysis with baseline models highlights the system’s effectiveness, achieving a mean absolute error (MAE) of 0.89, mean squared error (MSE) of 2.52, and root mean squared error (RMSE) of 1.57, with an occupancy prediction accuracy of 99.6%. This solution serves as an efficient parking enforcement mechanism, reducing congestion and optimizing urban parking availability.