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Traffic congestion has become a major concern, aroused as a result of increased population and urbanization. Hence, novel and innovative methods for controlling ever-increasing traffic volumes are essential. Conventional traffic light schemes are the most popular method of controlling traffic, and it is logical and economical to make research endeavors to optimize their existing performance. Despite numerous studies, the aforementioned problem has not been optimally and sufficiently solved. In this research, we introduce an adaptive traffic signaling scheme based on vehicle density to facilitate optimal traffic signal control as well as effective traffic management. We also propose effective coordination of the traffic amongst the junctions. Here, the live video is utilized as an input provided to a deep Q network to provide adaptive phase timings as the output. In the proposed scheme, we introduced per car unit (PCU) as a novel parameter to represent the effect of each vehicle type on traffic conditions. Numerous filed trials on real-time data amply prove that the proposed scheme enhances the average speed of traffic up to 5.597 km/h. The proposed scheme shows an average increment of 175.71% in average mean speed compared to the existing static schemes. Except for the high traffic scenario, for both mid traffic and low traffic scenarios, the proposed scheme shows a considerable improvement in both average densities and maximum densities. In the mid-traffic scenario, the average speed shows an improvement of 3.85 km/h, while in the low traffic scenario, the average mean speed shows an improvement of 7.96 km/h. A reduction in fuel consumption and average delay were also observed, which will lead to a greener Transport 4.0.