Commuters lose a lot of time and effort due to the inefficiency of traffic management. Although nowadays most of the processes are automated, it seems a speed violation detection is the least focused area apart from using speed guns which the RADAR may make mistakes and yet, doing such will benefit people by saving their time and let them escape from the troublesome situations. To address this issue, a real-time solution by fully automating the process of detecting the speed violation and the license plates of the offenders is proposed in this paper. A vehicle approaching a specific area will be automatically identified and tracked from a reference starting point. Within the covered range of the camera according to the traffic density present at that instance, the maximum speed for a vehicle is estimated and the vehicles that exceed the stipulated limit are identified as a violation. The core part of the proposed system is license plate recognition. To properly extract the license plate with the best view to proceed with the identification process is another problem that needs to be focused on. We utilized deep neural networks in a novel way for the aforesaid purpose. As these neural networks consist of numerous parameters, we utilized GPU for processing to gain smoothness in real-time. Using our novel segmentation free license plate identification method which utilizes object detection principle to fully capture the speed violation along with its offender. Numerous field trials proved that the proposed methodology provides far superior performance levels compared to the conventional systems and the other existing methodologies, which will certainly cater to the demanding requirements of Transportation 4.0.