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The number of global road traffic accidents is rising every year and remains undesirably high. One of the main reasons for this trend is that, in many countries, road users violate road safety regulations and traffic laws. Despite improvements in road safety legislation, enforcement is still a major challenge in low- and middle-income countries. Information technology solutions have emerged for automated traffic enforcement systems in the last decade. They have been tested on a small scale, but until now, the cost of deployment of these systems is generally too high for nation-wide adoption in low- and middle-income countries that need them the most. We present the architectural design of a traffic violation enforcement system that can optimize the cost of deployment and resource utilization. Based on the proposed architecture, we describe the implementation and deployment of the system, and perform a comparison of two different versions of the video-based enforcement system, one using classical computer vision methods and another using deep learning techniques. Finally, we analyze the impact of the system deployed in Phuket, Thailand from 2017 to the present in terms of local road users’ compliance and the road safety situation. We conclude that the system has had a positive impact on road safety in Phuket at a moderate cost.