African cities face unprecedented urban growth challenges, yet lack adequate transport demand models to guide mobility policies effectively. Traditional approaches rely on household travel surveys, which are scarce and outdated in most African contexts. This research explores innovative data sources, particularly camera-based counting data processed through computer vision techniques, to address this critical gap. We present a comprehensive methodology combining mobile phone data, traffic counting from video streams, and machine learning approaches to generate realistic travel demand patterns. Our experimental validation in Libreville, Gabon, demonstrates the feasibility of using commodity smartphones with pre-trained object detection models (YOLOv8) to extract traffic flow data. The results show promising accuracy in vehicle detection and counting, with potential applications for origin-destination matrix estimation and transport planning. This work contributes to the growing field of data-driven urban mobility analysis in developing countries, offering cost-effective alternatives to traditional survey methods while maintaining scientific rigor in transport demand modeling.