Driving Global Road Safety Innovation with AI
While most people might see the morning commute as just another grind, road safety experts know it paints a far grimmer picture. Every day, thousands of lives are impacted by traffic collisions, and for children and young adults, these incidents remain the leading cause of death worldwide. But out in the sun-soaked labs of Arizona State University (ASU), a group of forward-thinking researchers are reimagining road safety with the help of artificial intelligence.
Their mission? To make streets smarter, safer, and more resilient — all while respecting privacy and pushing the boundaries of engineering.
A smarter way to see the road
At the School of Computing and Augmented Intelligence, Associate Professor Yezhou “YZ” Yang is steering computer vision into new territory. For the uninitiated, computer vision is a field of AI that allows machines to interpret and respond to visual information, much like humans do — but often faster, and with fewer mistakes.
Yang’s team has developed eTraM, short for Event-based Traffic Monitoring. Unlike traditional traffic cameras that endlessly record video footage (often with dubious results and mounting privacy concerns), eTraM does something smarter. It records data — not images.
Think lighting conditions, weather, road activity, and near misses. All of this without capturing a single identifiable face or licence plate.
“The cities of the future must address concerns about personal privacy and security” says Yang. “eTraM records data and not images, providing information that can help train AI models but doesn’t use anyone’s image without their knowledge.”
Already installed around ASU’s Tempe campus, eTraM sensors are soaking up insight on how roads behave at night, in poor weather, and during peak congestion. The goal? To empower urban planners to predict high-risk zones before they become crash sites.
Machine learning in the driver’s seat
To supercharge the potential of eTraM, Yang has joined forces with Assistant Teaching Professor Bharatesh Chakravarthi. Their mission: build machine learning models that can crunch the data and spit out tangible recommendations.
By analysing patterns in traffic behaviour, these models can help local governments make better decisions about where to place traffic lights, pedestrian crossings, signage, and even redesign entire intersections. Rather than relying on reactive planning after a crash occurs, cities can now use predictive insights to shape safer public spaces.
This data-centric approach aligns perfectly with the vision of smart cities and digital twins — virtual replicas of physical systems that allow planners to simulate different scenarios before breaking ground.
Global collaboration accelerates impact
Taking cutting-edge research and turning it into actionable policy doesn’t happen overnight. That’s why Professor Kamil Kaloush is putting his foot on the pedal. A pavement engineering expert from ASU’s School of Sustainable Engineering and the Built Environment, Kaloush is also chairman of the board at the International Road Federation (IRF Global).
Through his work, Kaloush is spreading ASU’s AI insights far beyond Arizona’s borders. In collaboration with colleagues including Yang and computer science assistant professor Hua Wei, Kaloush co-developed an international workshop designed to demystify AI for transportation professionals.
“Our mission is to advance the transfer of knowledge among road and transportation professionals and experts, ensuring the development of safe and sustainable infrastructure” says Kaloush.
From Abu Dhabi to Phoenix
In April, the team launched their flagship workshop: “AI and Big Data Applications for Future Traffic and Incident Management” at a two-day event in Abu Dhabi. Co-hosted by IRF Global and Abu Dhabi Mobility, the event brought together city officials, engineers, researchers, and private sector tech leaders.
Behind the scenes, coordination was led by Aliaksandr Smirnou, IRF Global’s director for the MENA region. On the ground, Mohammad Farhadi Bajestani, an ASU adjunct faculty member, delivered hands-on sessions. Other ASU faculty contributed remotely, walking attendees through case studies and live demonstrations.
The sessions focused on:
- AI-driven traffic forecasting and congestion mitigation
- Emergency response optimisation
- Designing data-driven infrastructure for autonomous vehicles
- Training attendees to build their own machine learning models
The goal wasn’t just to share research, but to build confidence in AI as a practical toolkit for change.
Next stops and expanding ambitions
Feedback from the Abu Dhabi sessions has been overwhelmingly positive, and the ASU team is already gearing up to deliver similar workshops in new global locations. With IRF Global as a strategic partner, these AI-centric events are set to reach transportation leaders in dozens of countries.
Closer to home, discussions are underway to pilot the workshop in Phoenix. The idea is to take the same tools that impressed international planners and apply them to real neighbourhoods in Arizona — ensuring that local decision-makers benefit from the home-grown innovation.
Yang sees this as just the beginning. “Now it’s back to work. No pit stops!” he quips.
Bridging the AI gap in transportation
Despite its promise, the integration of AI into transport systems still faces challenges. There’s hesitancy among some policymakers, budget constraints in smaller municipalities, and a general lack of AI literacy in the infrastructure sector.
That’s precisely why the ASU workshops are so important. By giving planners the opportunity to roll up their sleeves and experiment with machine learning, the training helps close the skills gap.
It’s also a move that fits squarely with IRF Global’s larger strategy. The organisation is committed to raising standards in road safety, engineering, and transportation policy across more than 70 countries.
Safer streets, smarter cities
From the lab to the boardroom to the intersection, ASU’s AI-powered road safety revolution is already creating ripples. With tools like eTraM offering anonymised, event-based data collection, and workshops building global AI capacity, the pieces are falling into place.
Yang, Kaloush, and their team are proving that innovation doesn’t just happen in code. It happens through collaboration, education, and shared ambition.
If all goes to plan, future drivers might not even notice the AI quietly orchestrating safer roads around them. But their lives may very well depend on it.
Full speed ahead for global traffic safety
With Arizona State University acting as a catalyst for AI-driven innovation in transport planning, the horizon looks bright for a new generation of safer, smarter streets. The ripple effect of their work is already spanning continents — from Abu Dhabi to America, and from classrooms to city halls.
Now the challenge is clear: to keep the momentum alive, build on these foundations, and ensure that artificial intelligence becomes a reliable co-pilot in designing the roads of tomorrow.
