Adaptive Traffic Lights Control Using Iot And Image Processing

Abstract

Urban Traffic Congestion Has Become One Of The Major Issues In Modern Cities. As City Populations Grow And Vehicle Usage Increases, Existing Road Systems Struggle To Manage The Traffic Load Effectively. This Results In Long Delays, Energy Wastage, Increased Pollution, And Decreased Mobility. Traditional Traffic Control Methods Rely On Fixed-timing Signals That Follow Preset Schedules Without Considering Real-time Traffic Density. This Leads To Significant Inefficiencies And Commuter Frustration. To Tackle This Issue, We Propose An Intelligent Traffic Signal Control System That Dynamically Adjusts Signal Durations Based On Current Traffic Conditions. This System Integrates Internet Of Things (IoT) Devices With Image Processing Techniques. Specifically, Haar Cascade Classifiers Are Used To Detect Vehicles And Measure Traffic Density Efficiently. By Adapting Signal Timings According To Actual Traffic Flow At Intersections, The System Ensures Smoother Vehicle Movement And Minimizes Unnecessary Delays. The Use Of Decentralized, IoT-based Microcontrollers Enhances The System’s Flexibility And Reduces Reliance On Central Servers, Making It More Resilient In Practical Deployments. Simulated Results Demonstrate Notable Improvements In Reducing Traffic Delays, Optimizing Resource Use, And Enhancing Travel Experiences. This Adaptive Approach Paves The Way For Smarter Cities, Contributing To Reduced Environmental Impact And Improved Urban Quality Of Life.

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