Can we Make Traffic a Thing of the Past (and greatly improve supply chains)

Traffic Management outlook. 

Making Traffic a Thing of the Past  By Logan Kugler

Communications of the ACM, January 2023, Vol. 66 No. 1, Pages 19-20  10.1145/3570519

Americans wasted a whopping 3.4 billion hours in 2021 thanks to traffic, according to research from connected car analytics company INRIX, which also noted that this equates to 36 hours lost per person. The numbers are clear: Even with drops in traffic thanks to new travel patterns in the wake of the pandemic, we still lose an entire workweek each year to traffic.

Soon enough, artificial intelligence (AI) may be able to alleviate—or fully solve—the problem.

Today, researchers and companies are working to develop AI-powered systems that tackle the problem of traffic from a number of angles.

For instance, Intelligent Traffic Control of Tel Aviv, Israel, has developed a solution that collects data from traffic cams, then regulates traffic signals to optimally route vehicles. Paradigm Traffic Systems of Texas offers a range of traffic management products to manage intersections and freeways.

However, a team of researchers at the U.K.'s Aston University has taken things one step further. Their AI traffic regulation system does not just use data from existing traffic cams to manage vehicle flow at a specific intersection; it has learned, from a traffic simulation, how to optimally regulate traffic in real time at real-world intersections—even in traffic situations it has never seen before.

These capabilities are all made possible thanks to advancements in a number of different AI fields, including computer vision, machine learning, and deep learning.

At its core, AI looks to automate some of the steps of the human decision-making process in traffic management, such as detecting vehicles or identifying problematic traffic patterns.

Many of today's AI traffic management solutions focus on the detection and classification of traffic, says Maria Chli, a researcher at Aston University who works on AI for traffic management. That means learning to recognize different types of road users, such as distinct types of vehicles and pedestrians, by processing data from magnetic loops buried in road surfaces, traffic cameras, and LiDAR systems. These detection and classification systems are often coupled with simulation technologies so they can quickly and easily use what they capture to model that information in order to teach traffic management systems how to identify traffic patterns, detect incidents, and monitor a variety of road conditions.

"These types of technologies work on top of the existing tech stack of cars, roads, and traffic lights, but seek to make the optimal interventions that reduce car waiting times, emissions, and energy consumption," says George Vogiatzis, a computer vision researcher at Aston University who also works on AI traffic management systems.

If they work as intended, AI-powered traffic management systems could save humanity billions of hours every year, and significant amounts of fuel.  ... '