Rule Based Reasoning For Traffic Monitoring On Terrestrial And Aerial Capture Of Traffic Scene In Real Time Kinematics

This paper work focuses on rule-based reasoning of aerial traffic scenes captured and its processing for monitoring vehicles in urban areas. This rule-based machine learning model analyzes traffic patterns, trained on extensive aerial datasets for improved accuracy and adaptability in real time. This integrated approach, evaluated through real-world testing, advances intelligent transportation models significantly by majorly affecting traffic management, controlling congestion, and enhancing security and safety measures. The model proposed to standardize a domain’s compliance process may in principle, be used to standardize compliance processes in other domains. The proposed methodology will assist in diagnosing congestion and improving traffic conditions by proposing a common vocabulary and reasoning for traffic monitoring. The proposed approach manages operational problems by disentangling traffic problem detection, solution, and conflict management. The use of an inference engine with floating rules to be adopted by the different patterns helps in real-time replication of human vision and decision-making capability.