Open a news site or turn on the TV, and chances are you’ll hear about the next great artificial intelligence innovation. AI can draft slide decks in seconds, return online purchases, or even create a travel itinerary from scratch.
Chatbots and copilots are dominating the AI conversation. These consumer AI tools are often judged by engagement and novelty, and many come and go quickly without creating any meaningful impact.
Meanwhile, the AI being built into systems that move people, freight, and goods is flying under the radar. Conversations around infrastructure AI may attract less attention, but judging by their reliability, accuracy, and measurable operational impact, these are the AI systems that will power economic results for years to come.
A true test of AI impact: Transportation
When most people think of AI and transportation, autonomous vehicles pop to mind. But beyond still-emerging AV technology, AI is working behind the scenes to solve some of transportation’s most complex systemic problems.
Millions of Americans struggle with traffic congestion, and even more are impacted by the pollution that it exacerbates. Congestion cost American drivers $85.8 billion in lost time last year. The average driver lost 49 hours sitting in traffic.
Those losses in time and money are staggering. At its core, congestion is an economic efficiency problem. Cities, transportation agencies, and supply chains are all turning to infrastructure AI to solve it.
Infrastructure AI can combine billions of real-time data points from vehicle movements and signal timing with historical data, creating instant insight into traffic conditions. Using AI, cities and transportation agencies can not only identify high-risk zones throughout traffic networks before accidents happen, but they can also find the best ways to address those risks to make roads safer for everyone.
In short, infrastructure AI allows agencies to move from a reactive traffic management system to a forward-looking, proactive system, one that moves people and freight through networks more quickly and safely.
Measured in safety and efficiency, not productivity
While infrastructure AI systems operate silently in the background, they shape the way people and freight move through crowded traffic networks. However, these systems’ effects aren’t measured in productivity. The real results of infrastructure AI can be seen in improvements in safety and efficiency.
Cities and transportation agencies are using AI to gain real-time insights into traffic networks. These insights can identify congestion patterns in real time and predict bottlenecks before they form.
Predictive analysis fuels some of today’s most effective traffic management solutions. Real-time data that predicts congestion can help agencies create dynamic lane management, opening extra lanes during peak travel times and changing two-way streets to one-way corridors to help move traffic efficiently and safely.
Real-time insight into congestion also allows agencies to make high-speed highways and interstates safer. Infrastructure AI can identify pockets of severe congestion, automatically triggering warnings to upstream drivers that they are approaching slow or stopped traffic. These warnings can prevent “shockwave” accidents, which are often deadlier than the original accidents that caused the congestion.
Transportation agencies have traditionally dealt with congestion and safety risks retroactively. Management strategies were based on periodic traffic studies and planning built from historical data. Infrastructure AI helps agencies move to a more dynamic, predictive management philosophy, one that anticipates problems and risks before they emerge.
Using AI, cities and agencies can identify underperforming corridors and more efficiently allocate resources to address root issues. The same is true for deploying resources in response to winter storms and other events that demand immediate and urgent response. The more efficiently resources are deployed, the greater the gains in efficiency can be.
Finally, infrastructure AI is changing the way cities and agencies manage safety in traffic networks. Real-time insights, combined with historical data, help traffic engineers identify high-risk corridors and intersections. This predictive approach allows agencies to create data-driven interventions to make high-risk areas safer before they cause accidents.
Nowhere is this predictive approach to safety more important than the curb. As more autonomous vehicles are introduced into traffic networks, cities will need to better manage curb regulations.
The curb is critical infrastructure for rideshares, deliverers, and mobility services. It is also one of the riskiest places for vulnerable road users like pedestrians and cyclists. As more AVs come online, cities will need both historical and real-time data to safely manage the intermingling of different users at these busy nexus points.
At its core, infrastructure AI is a tool to help make traffic management less reactive and more predictive, improving the efficiency and safety of transportation networks for everyone.
AI’s value is shifting to operational intelligence
Systems that enable real-time decision-making are becoming foundational to modern cities and transportation networks as AI’s value is more often found in operational intelligence rather than novelty.
Cities and transportation agencies are increasingly valuing continuous measurement to boost efficiency. Infrastructure AI offers the real-time data needed for such measurement.
As a solution, it is cost-efficient and scales easily.
Gathering and analyzing real-time and historical traffic data doesn’t require expensive hardware like other solutions. But it does allow cities and transportation agencies to move from reactive traffic management to a proactive approach.
The continuous measurement made possible by infrastructure AI delivers measurable results, unlike many consumer AI products. Because infrastructure AI’s results are quantifiable, agencies can easily document improvements in safety and defend their ROI. Its real-world results will help infrastructure AI define the next decade.
The novelty is already fading around some of the more superfluous consumer AI products. When those tools are long forgotten, it will be the AI infrastructure systems operating invisibly behind everyday life that will produce the greatest long-term impact in our lives.
Ahmed Darrat is a trained Transportation Engineer with over 20 years of experience in transportation policy, operations, and technology. At INRIX, Ahmed leads the product management team, which is responsible for delivering data and SaaS applications in the curbside, location intelligence, safety, and traffic operations verticals. Prior to INRIX, Ahmed worked in engineering, policy, and operations roles at the City of Seattle for 10 years, culminating in his role as the transportation policy advisor to the mayor. Between INRIX and Seattle, Ahmed supported both the public and private sectors globally as a consultant at Cityfi and Transpo Group.
