AI Powered Computer Vision-Based Framework For Real-Time Road User Classification And Intelligent Traffic Signal Control
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Date
2026-06-04
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Publisher
Uganda Christian University
Abstract
Traffic congestion is a leading cause of economic and productivity loss in Uganda. The Greater Kampala Metropolitan Area carries roughly half of the country’s registered vehicles, and peak-hour speeds on central corridors drop to as low as 11 km/h. At the same time, more than 70% of road traffic fatalities in the country fall on vulnerable road users, that is pedestrians, cyclists and boda-boda motorcyclists. Most of Kampala’s signalised junctions still operate on fixed-time plans that cannot respond to real demand, and none of them offers cyclists a dedicated signal phase. This report describes RoadWise, a group final-year project that builds a low-cost, AI-powered traffic management framework for classifying road users in real time and controlling intersection signals dynamically. The system is organised in four layers: a sensing layer made of USB cameras and IoT sensors, an intelligence layer that runs a YOLOv8 detector and a Firebase cloud backend, an action layer that drives smart traffic lights, and an interface layer made of a traffic officer dashboard and a road user mobile web app. A custom scale model traffic setup was built, covering both three-way and four-way junction layouts, so that the full detection to actuation loop could be tested under controlled conditions.The work was grounded in a PRISMA compliant systematic literature review that screened 4,419 records and retained 12 primary studies. The review confirmed that, although YOLO based vision and adaptive signal control are individually mature, no reviewed system offers a dedicated cyclist priority phase in mixed traffic. RoadWise closes that gap through what we call a blue light phase, which is activated whenever cyclist presence at a junction passes a configurable threshold. On the prototype, the detector reached 100% vehicle detection and 85% cyclist detection accuracy under varied lighting, with a best validation mAP@0.5 of 0.977 on the miniature model dataset. The adaptive controller reduced junction waiting times by up to 40% compared with a fixed time baseline, and the web to hardware synchronisation protocol achieved 100% state consistency over serial acknowledgement feedback. The same controller was shown to run simultaneously across three-way and four-way configurations, which supports scalability toward real junctions.
The report also discusses the limits of the work, in particular the scale model-to-real domain gap, the narrow class schema, and the remaining steps needed before a pilot deployment on a real KCCA junction can be attempted.
Description
Undergraduate
Keywords
Intelligent Traffic Management, Computer Vision, YOLOv8, Adaptive Signal Control, Vulnerable Road Users, Cyclist Priority, IoT, Smart City, Kampala, Uganda