Signal controlled intersections are of huge importance to mobility and safety all over the globe. Currently used traffic control solutions based on the punctual/spot/inductive coils detection-based solutions becoming outdated. Not intelligent enough to face challenges of the today’s traffic conditions or to support sustainable development strategies. Urbanization growth expected +35% by 2030, increased maintenance effort and lack of resources in the road administration makes it even harder to keep the control strategies at the intersection up to date. 

Presented innovative, adaptive, self learning traffic control solution is a reply to most of the challenges listed above. Takes an advantage of technology development in electronics, object tracing and connected vehicles area. Allowing us to look at the traffic control task from a different perspective and introduce fresh approach with algorithms capable to make use of the new data sources. Presented case study proves efficiency improvement in terms of average delay, number of stops and the C02 emission reduction at signalized intersection in different, unexpected traffic conditions. 

Let us share with You our experience from developing modern, artificial intelligence based adaptive traffic control solutions. Based on the object tracing detection data. Including farther explanation to the cost function, user interface and VISSIM simulation. Summarized with traffic control efficiency results benchmark comparing wildly used vehicle actuated traffic control, punctual detection based adaptive versus real time object tracing based adaptive control solution.