Comparative Analysis of Deep Learning Algorithms for IoT-based Smart Outdoor Parking Systems using Weighbridge Load Sensors to Enhance Efficiency, Safety, and Convenience in Smart Cities

This research work explores the significance of improving outdoor parking efficiency, safety, and downright convenience—particularly in those crowded urban areas we often refer to as smart cities. It starts by looking at several deep learning algorithms, using data from devices like weighbridge load sensors, and instead of following a straight order, it changes things around to highlight how various approaches compare. When viewed side by side, the study spills out a slew of figures—parking patterns here, sensor readings there, performance stats all over—which imply that certain AI models truly outperform the old-school techniques in forecasting how full spots get and reducing the lag in notifying open spaces. Usually, this translates to a smoother trip for motorists. Interestingly, when cars idle less because of these better technologies, air quality might improve and traffic flow could grow a bit less chaotic, suggesting good side benefits for public health. This concept expands the debate somewhat to imply that smart parking is a major driver in sustainable urban living rather than only a cool tech gimmick. All things considered, the study sets a foundation for future research at the intersection of artificial intelligence and urban health, suggesting that married together, smarter infrastructure and improved living circumstances could significantly alter how densely populated regions operate.