Amidst the bustling economic hub of Nigeria, known as Lagos, traffic congestion remains a persistent challenge that plagues the city, impeding the economy, health, and safety of its residents. Professionals spend a staggering 90% of their work time navigating traffic, resulting in stress and anxiety. Some are robbed in the process, even in broad daylight. To address this problem, this work is done to help government agencies optimize traffic management and provide road users with the necessary information to navigate the city safely and efficiently and make informed decisions about which routes to take.

Using machine learning techniques, we were able to identify, both historically and in real-time, traffic hotspots, accident-prone areas, and traffic flow patterns around the city. With this analysis, government agencies can develop traffic management strategies that prioritize safety and efficiency, such as rerouting traffic away from congested areas or implementing safety measures at accident-prone or traffic robbery-prone locations. Furthermore, traffic-free routes can be provided in real-time to commuters based on their location. This work has the potential to reduce traffic congestion and improve road safety, leading to economic and health benefits for Lagos residents. The results of this research not only have practical implications for the city but can also be extended to other urban areas that have traffic agencies and are also facing similar challenges.