*Result*: The role of computer vision in developing next generation smart transportation systems: A review.

*It is important to note that while the trends towards incorporating autonomous automobiles into populated concrete jungles constitute a challenge to current traffic control, the use of computer vision has become vital in today's automobiles. As it was mentioned earlier, research works have established that integration of computer vision systems, Artificial Intelligence (AI) and Machine Learning (ML) algorithms helps in reducing the road accidents, improving traffic congestion and transportation facility. Here we use the traffic cameras and sensors, ITS connectivity of vehicles and various other sources of real-time data and build the dynamic model for real-time monitoring of the traffic, timely identification of the traffic incidents, and relevant strategies for the triggering of appropriate stakeholder actions. Consequently, these results suggest that although computer vision solutions can yield significant improvements in STS, their applications face significant limitations in data handling and storage, algorithms precision, and privacy and cost of infrastructure regulations. The study calls for a multi-disciplinary strategy to build the appropriate legal regime and ethics required for safe usage of computer vision in transport. This research therefore; offers a brief analysis of the modern smart transport technologies, identifies needs that are potentially unmet noting areas that require further innovation and/or policy/legislation change. [ABSTRACT FROM AUTHOR]*