Death, taxes — and traffic — are some of life’s unavoidable certainties. But in the quest for smarter, more efficient urban environments, artificial intelligence (AI) is helping to pave the way for significant advancements in traffic optimization by transforming intersections into smart, cloud-connected grids.
After all, AI can process complex datasets much faster than any human or traditional computer system could ever hope to, offering unprecedented insights into traffic patterns and enabling dynamic responses to changing conditions, which makes it an invaluable tool in the growing traffic management toolkit.
“AI is one component in a broader, complex and connected network, but it gives several advantages, including the ability for the system to learn,” Tal Kreisler, CEO and co-founder at NoTraffic, told PYMNTS for the PYMNTS “AI Effect” series.
That’s because the advent of AI in traffic management represents a foundational departure from traditional, rule-based systems toward adaptive, learning-based approaches.
“Think about it as something that works like a chess game calculating millions of scenarios in every given second and deciding about the next best thing to do considering what happened locally at the intersection, considering who is coming from the other intersections nearby, and subject to policies defined by the agencies such as prioritizing pedestrians, or public transportation next to schools public transportation, emergency vehicles, and so on,” Kreisler said.
And cities around the globe are increasingly turning to AI technologies to reduce congestion, enhance safety and improve the overall quality of urban living.
While traffic management stands as a persistent challenge, one that has significant implications for efficiency, sustainability, and safety, the fusion of AI and connected vehicles — not just automobiles — presents a promising solution to the thorny problem of traffic and congestion.
“The majority of traffic lights both in the U.S. and also around the world are still operating based on based on timing plans, meaning that they do not have a lot of room for flexibility … and these systems were not designed to provide answers to the types of needs that modern intersections have around things like delivery vehicles, e-bikes, micro mobility, and other forms of mobility,” said Kreisler.
“The way we see it … once we have sensing devices deployed, then we can transform the intersections into a dynamic network that can operate in a holistic way that allows agencies to define various policies,” he added, noting that this capability ensures that customized solutions can be tailored to diverse traffic scenarios, including extreme weather conditions and varying traffic patterns.
The benefits of AI-powered traffic management extend far beyond mere optimization. By dynamically adjusting traffic signals based on real-time data, cities can significantly reduce delays, enhance pedestrian safety, mitigate congestion and improve overall traffic flow.
“If we want to prioritize pedestrians next to school in the morning, if we want to prioritize public transportation, emergency vehicles, side street versus main street, those types of policies can be defined and implemented. And from that point on, we should allow the system to run the network in the most efficient way to reduce delay, increase the throughput, but also serve these types of policies,” said Kreisler.
NoTraffic’s technology leverages edge computing, empowering sensors to analyze data autonomously and continuously improve models. The company’s collaborations with cities like British Columbia, Phoenix and Tucson have demonstrated remarkable outcomes, including substantial reductions in pedestrian wait times, vehicle accidents and fuel consumption.
Still, the widespread adoption of AI-driven traffic solutions faces many challenges related to technical readiness and regulatory compliance.
“One of the biggest benefits that I’m particularly excited about is the fact that we are transitioning from an industry of devices to an industry of software defined infrastructure, meaning that you don’t have a bunch of devices anymore fighting with integration … you have a single hardware platform and on top you have a layer of applications,” Kreisler said. “Allowing the interconnectivity between different solutions is the bigger picture.”
Looking ahead, he envisions a future where AI and connectivity synergize to create a more efficient and safer urban environment. Beyond traffic management, the integration of AI with diverse mobility modes, such as e-bikes and scooters, holds the promise of further enhancing transportation systems’ adaptability and responsiveness.
“The environment is becoming much more complex to manage, which is where more advanced technologies come into the picture, and why you need data,” Kreisler said.
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