Since decades, traffic signals have been used to regulate traffic in order to increase efficiency in vehicular movement and road safety. These conventional traffic lights have done the job required with aplomb. However, increase in population, an exponential increase in traffic and the randomness in the traffic density pattern throughout the day has rendered these conventional signals rather obsolete.

The conventional traffic signals work on pre-set clocks having a fixed time period to switch traffic between different directions. They lack the capability required for smart functioning and do not consider the real-time demand of commuters as input, hence, do not consider adaptive traffic optimization. Vehicles, thus have to wait for a long-time span even if the traffic density in their lane is very less. In short, the two main functionalities where a conventional traffic system falls short are, traffic density and dynamic signal time changes based on it.

The answer to this problem is Adaptive Traffic Control System (ATCS), an intelligent traffic management system that senses the changing traffic patterns around the traffic signal and other nearby junctions to generate an optimized cycle time plan. This system can reduce the traffic congestion and also increase the average traffic speed. However, replacing conventional traffic signals with ATCS setup is very taxing to the city exchequer given its high cost and the amount of civil work involved.

A low cost yet equally effective alternative is reusing existing smart elements deployed in a city to integrate conventional traffic signals with ATCS network with the support of a secure data sharing platform such as IUDX. These existing smart elements are the surveillance and traffic violation detection systems viz. RLVD/Surveillance Cameras, one of the solutions of Smart City 1.0, that have been mounted at multiple traffic signals across smart cities. In this arrangement, one of ways of generating traffic density in real-time can be to perform video analytics on the feeds from existing surveillance/violation cameras. In another approach, third-party traffic data like google real-time traffic data can be used. Of course, the latter will be more expensive for a smaller number of traffic junctions.