Reducing Traffic with Computer Vision

Transport for Cairo (TfC) is a data-driven consultancy that focuses on improving urban mobility in emerging African cities by addressing traffic congestion and rapid urbanization, particularly in the context of climate change. In a recent project in Uganda, where traffic data was scarce, TfC utilized computer vision to efficiently and accurately monitor traffic flows. This was made possible by leveraging existing closed-circuit TV infrastructure and developing custom models to recognize a variety of vehicles unique to the area, a task that pre-trained models could not accomplish. TfC used Roboflow to process labeled data, manage class imbalances, and augment datasets, which streamlined the training of their computer vision models. This approach not only saved time and resources but also enhanced the accuracy and reliability of traffic data collection, allowing TfC to optimize transportation systems and develop sustainable mobility solutions for the future.