AI Integrated System in Smart City for Traffic Police Optimization

Traffic management is highly essential task in road safety and crime presentation.  But the conventional mechanism to handle traffic is very cumbersome which is based on manual decision making, fixed schedules and intuition of the officers which in many cases results in a lack of effective use of resources and slow response time. It is very less effective in identifying criminal activities and traffic management. These limitations are enhanced over time in the fast-changing urban settings where traffic trends and hotspots of violations change dynamically over time. This paper is the solution to the problem which is AI based automation system of tactical deployment of police checkpoints aimed at maximizing the presence of police points based on predictive analytics and real time operational intelligence. The framework includes machine learning engine based on gradient boosting algorithm to estimate the probability of violation and spatial clustering methods to detect geographic regions at high risk. A web-based command center dashboard is a real time visualization of dynamically generated heatmaps, violation notifications, and deployment suggestions with a mobile application allowing field officers to get alert messages and track their geographical locations on the control center.