With the rapid growth of urban areas, the conventional road network finds it increasingly difficult to cope with the overwhelming number of vehicles on its streets. Drivers waste valuable time waiting in traffic bottlenecks, leading to significant financial losses, higher CO2 emission levels, and severe daily stress.
Cities have relied upon rigid programmed timers that have always worked in the same way, whether the road was empty or crowded. Artificial intelligence is changing everything nowadays; introducing AI traffic management into use cities transform their streets into highly adaptive transportation networks.
To explain the transformation that technology makes in our lives, let’s examine the constraints of the old world transport system. Traffic signals of the conventional road network follow a certain loop.
If the street receives thirty seconds of green light, it stays green during that whole period, even when there are no cars on the side street, forcing all the cars going down the major highway to stop.
Intelligent road system discards all these inflexible patterns, replacing them with flexible and adaptive monitoring of the real situation in the physical environment of the city.
Highly evolved traffic infrastructure operates as a brain for the city streets. The functioning of such technology depends on the use of a multistage digital pipeline to help cities function efficiently.
High-resolution surveillance cameras provide a live video feed of the intersection. Special machine learning models, including YOLO, quickly analyze these images and provide an assessment of vehicle counts, lane queue lengths, and movements.
The technology assesses the level of traffic flow by categorizing it as low, medium, or high density. It also measures the number of cars, trucks, and buses that occupy certain lanes in real time.
Optimized traffic signals rewrite their schedules based on real-time information. Highly congested streets get a longer green light, while free lanes will have shortened cycles.
However, there is more to intelligent routing than the control of separate traffic lights. Unlike the relocation of congestion from one point to the next, intelligent routing involves coordination among neighboring traffic lights at once. Thanks to the analysis of patterns combined with live streams, it is possible for a network to foresee the incoming flow and create green waves.
It would be safe to say that the future of urban traffic lies entirely in the realm of network automation. The change from the outdated static light cycle system to the reactive AI-driven traffic system will help commuters cut waiting time in half, reduce exhaust emissions, and greatly increase emergency response speed.
