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Reality check
Where does AI fit into the mobility ecosystem?
SEPTEMBER 2025
ITSInternational.com
INTRODUCTION
AUTHOR: Jatish Patel is CEO of Flow Labs
Is AI capable of doing everything in transport? Does regulation restrict innovation? And wh
Where is transportation In late 2022, everything changed. ChatGPT hit the market and AI became real for the world. In the months that followed, we saw an explosion of innovation: copilots for developers, AI productivity tools, generative design platforms, all arriving at a pace that we haven’t seen in human history. Whole industries have been seismically shifted as they’re forced to adapt. AI wasn’t just theoretical anymore, it was in people’s hands and it was unlocking problem after problem. But in transportation…the silence was deafening. No new class of tools. No radical rethink of how we design, operate, and improve infrastructure. While other sectors redefined what AI could do, transportation got left behind. Where was our ChatGPT moment?
companies shattering benchmarks, delivering new breakthroughs, pushing innovation and each other forwards. But in transportation we got something different. Companies suddenly rushed to slap the letters AI onto preexisting products, riding the hype-wave even when the technologies used were barely related to AI. Companies that couldn’t even measure the past were claiming to now be able to predict the future. Then tech giants entered the transportation space, backed by trillion dollar market caps, they jumped in with big wallets and even bigger PR engines. They arrive with limited data, little domain understanding, and software teams trained to build consumer tools, not critical infrastructure. The results were underwhelming: models trained on narrow datasets, AI tools detached from regulatory reality built without even the most basic grasp of traffic engineering. Then we saw companies chasing novelty over necessity, deploying generative AI for tasks that demanded precision and
A Flood of Hype, and a Failure to Deliver After ChatGPT we saw a deluge of companies, both upstarts and big tech, developing similarly groundbreaking tools like Gemini, Claude, Perplexity, Grok, even DeepSeek. Every month we see these SEPTEMBER 2025
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here do engineers fit into it all? Jatish Patel of Flow Labs surveys the landscape, and asks…
n’s ChatGPT moment? repeatability, but instead simply allowed users to have conversations with bad data. They built interfaces that sounded futuristic, but didn’t align with how transportation professionals plan projects, follow standards, or secure funding. What looked like innovation quickly became a distraction. Then legacy hardware players pushed not for AI but on deploying even more hardware than we already have. At a cost that is unprecedented, impractical, and would drag us into deployment timelines that would last decades. Masquerading as innovations of the future, they’ve promoted the same iterations of past failures. By the looks of it we are far away from our ChatGPT moment. So how can we close the gap?
- 250PB of transportation data is generated each year in the US, but it sits trapped in siloes across multiple organisations, in multiple standards, or held hostage by protocols. Today most AI tools in transportation rely on a single stream of data, and they expect it to tell the whole story. That’s not how real systems work. Speed data alone won’t explain why delay is happening. Travel time can’t account for signal phasing. Crashes aren’t random— they’re shaped by volumes, behaviours, geometry, and policy. And then there are legacy vendors—particularly in the hardware space—who’ve built closed, proprietary ecosystems that make integration nearly impossible. These systems claim to champion openness, but often block agencies from accessing their own data. That asymmetry slows progress. AI thrives on interoperability. When critical infrastructure data is trapped inside black boxes, even the best AI systems are left flying blind. Data from multiple sources needs to be utilised to weave a complete picture of what is happening, to give AI the visibility it needs to make decisions.
Opening the Internet for Transportation ChatGPT and other LLMs benefit from a massive, open, and accessible internet providing petabytes of diverse and complete training data. Transportation is hampered by the opposite problem SEPTEMBER 2025
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INTRODUCTION
Making Regulation Work with Innovation
overrides logic. Theory breaks down the moment it hits the tarmac. And yet, this is how most innovation is still judged: not by what it delivers in the field, but by how well it aligns with legacy methodologies or academic models. That mindset has to change.
As organisations have struggled to keep up with the rapid advances in AI, the ones that have blocked tools entirely have fallen behind those that chose to embrace them, albeit with safeguards. Innovation in transportation doesn’t stall because of a lack of ideas—it stalls because the rules haven’t kept up. Many of the regulatory frameworks that agencies still operate under were created before modern computing, before spreadsheets, before real-time detection, and certainly before machine learning. These processes, often grounded in studies from the 1970s and 1980s, were built for a world where traffic counts were manual, plans were static, and engineering decisions were made on paper. They weren’t designed to block innovation - but today, that’s what they do. Even when a clearly better tool exists—more accurate, faster to deploy, easier to scale—agencies often can’t use it. The rules say no. Professionals are forced to decline cutting-edge technologies, instead using old processes that struggle with the demands of the fast-moving modern world. The system protects the status quo, not better outcomes. Meanwhile, much of academia continues to analyse transportation through simulation and theory. Models are developed with idealised assumptions: clean data, perfect deployments, cooperative jurisdictions, zero political friction. These abstractions rarely survive contact with the field. The real world runs on legacy firmware, patchy detection, and policy that often SEPTEMBER 2025
Perfection is the Enemy of Progress AI is not perfect by any means. We’ve all seen it hallucinate, or come up with bizarre answers to often simple questions. But the progress of these models is unquestionable, while many flaws remain countless more have been eliminated. Organisations that took the risk of early adoption and have stayed patient, have seen major benefits as models have improved. The most successful have been organisations who have spent time determining the limitations of these tools, accepting that they can’t perform everywhere but recognising and utilising them in the areas where they can. Real innovation is messy. It doesn’t happen by waiting until something is perfect. It happens by testing, learning, iterating, and improving. It happens by validating ideas in practice, not just publishing them in journals or pitching them in slides. Engineers need the space to experiment—not recklessly, but responsibly. Not for the sake of novelty, but in pursuit of better outcomes. The path forward isn’t about abandoning standards. It’s about updating them to reflect what’s possible. The burden shouldn’t be on agencies to defend every deviation from outdated practices.
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It should be an opportunity to prove what works—and to do it transparently, with data, in the open, and in collaboration with those who know the system best. Innovation doesn’t need to be perfect to begin. It just needs to be better—and provable. We’ve given the world new tools. Now we need to give professionals permission to use them.
reactive to proactive. AI should give people superpowers—not take away their agency.
This Is the Moment We’ve seen what real transformation looks like in other industries. Transportation is overdue. The principles we’ve championed over the years - integrated data, aggressive innovation, real-world experimentation, collaboration with engineers - are finally breaking through. Just this year, they’ve delivered breakthrough after breakthrough—from generating insights at scale across North Carolina in a matter of weeks without deploying a single piece of hardware, to launching Florida’s largest real-time integrated system, which has broken new benchmarks for real-time volume accuracy. And our biggest breakthrough innovation is yet to come later this year. These are the principles that will lead transportation to its ChatGPT moment. It is not a possibility, it is an inevitability. In a world of decreasing budgets, increasing complexity, and a growing despondency amongst the public we serve, the choices are simple. Who wants to build the future, and who wants to stay in the past? Who wants to build things that work, and who wants to cling onto processes that don’t? We know where we stand. The real question is, who’s coming with us?
Making AI Work for People, Not Making People Work for AI But the biggest myth in transportation right now is that AI can do it all on its own. That it can replace engineers, planners, and public decision-makers. A mindset that tries to work around engineers rather than with them, is a recipe for failure. We’ve seen this story play out before. Adaptive signal control was the first major foray into AI which promised to take retiming off engineers’ plates. But instead we saw engineers having to spend more time monitoring, maintaining, and managing these systems. Engineers shouldn’t be working for AI, AI should be working for engineers. Engineers aren’t bottlenecks, they’re the ones holding the system together. AI shouldn’t be here to displace them—it should be here to make them faster, sharper, and more effective. It can bring insights to the surface that would take months to analyse, simulate changes in real time, and move decisions from
The biggest myth in transportation right now is that AI can do it all on its own
SEPTEMBER 2025
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SMARTER AI FOR SAFER ROADS. The Iteris ClearMobility® Platform harnesses the power of AI to revolutionize road safety, enhance travel efficiency, and support thriving communities. Here’s how:
Proactive Intersection Safety: Our AI-driven predictive models, powered by real-time traffic data, identify high-risk intersections and deploy timely interventions, reducing crash risks before they happen.
AI-Enhanced Traffic Management: Using advanced AI technologies, our platform continuously analyzes traffic volume and incidents, optimizing traffic flow and minimizing delays across vast networks.
Optimized Traffic Incident Management: We process billions of GPS data points daily, using AI to translate complex traffic incidents into actionable insights, improving response times and precision in traffic management.
Intelligent Video Detection: With AI-powered video classification, our systems detect and classify road users with unparalleled accuracy, ensuring safer intersections and seamless integration with connected vehicles.
Iteris ClearMobility – AI at the heart of smarter, safer travel.
Explore how Iteris uses AI today
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Iteris ClearGuide Signal Trends: AI-Powered Transformation in Proactive Traffic Management Last year, Iteris introduced ClearGuide® Signal Trends, a breakthrough solution that gives transportation agencies unprecedented, near real-time visibility into how their traffic signals are performing—without the need for costly physical infrastructure or data collection at the intersection. This year, we’ve redefined what’s possible by integrating artificial intelligence-powered anomaly detection—enabling agencies to anticipate and resolve signal issues before they escalate, and transforming reactive operations into proactive intelligence. ClearGuide Signal Trends uses anonymized, cloudconnected vehicle trajectory data to monitor movement-level performance at intersections every 5 to 15 minutes. This means agencies can now spot abnormal traffic patterns—like arrivals on green, delays, or split failures—as they emerge, without relying on costly field devices or manual data collection.
AI can proactively flag rapidly degrading conditions, giving the agency time to adjust signal timing and manage detours more effectively. In Philadelphia, ClearGuide Signal Trends helped identify traffic disruptions caused by two major political campaign events in October 2024. The system detected a spike in control delay and split failures at a key intersection near I-95—an area heavily impacted by diverted traffic from the events. Thanks to the AI-powered anomaly detection, the severity of the degrading traffic conditions was identified in near real-time, providing the agency with actionable information that can be used to adjust signal timing to reduce congestion and improve flow during peak disruption periods.
Signal Timing Optimization Recommendations Informed by AI Historic intersection performance models, proven optimization algorithms, and AI combine to deliver targeted, data-driven recommendations for enhancing traffic signal operations. This includes offset optimization, cycle length adjustment, and split reallocation. After implementation, Signal Trends offers detailed metrics for the before-after evaluation of the improvements’ impact; see this intersection example in Texas:
Smarter Signal Operations Without Inf rastructure Traditionally, monitoring traffic signal performance required expensive sensors, on-site equipment and connectivity. ClearGuide Signal Trends upends that model by using cloudconnected vehicle data to deliver key performance metrics such as: • Arrivals on Green (AOG): The percentage of vehicles arriving during a green light. • Control Delay: The average delay caused by the signal itself. • Split Failures: Instances where vehicles wait through more than one signal cycle. • Turning Movement Percentages: The proportion of vehicles making specific turning movements at an intersection during a defined time period. These metrics are updated frequently and displayed in a user-friendly dashboard, allowing agencies to pinpoint inefficiencies and optimize flow in near real-time. .
The Future of Traff ic Management
AI That Spots Trouble Before Travellers Do
As cloud-connected vehicle data becomes more widespread, analytics solutions like ClearGuide Signal Trends are revolutionizing how agencies manage traffic. By combining realtime data with AI-powered insights, Iteris is helping cities and states shift from reactive to proactive traffic operations, improving mobility, safety, and the overall travel experience. ClearGuide Signal Trends is available to any agency in the U.S. and is already making a difference in communities large and small. With AI now in the driver’s seat, the future of traffic signal management is not just better—it’s transformative. Learn more about how Iteris is using AI to enhance roadway safety and operations at connect.iteris.com/ai-resources.
The newest enhancement to ClearGuide Signal Trends, AI anomaly detection, compares current traffic conditions to historical patterns and flags intersections where performance is deteriorating. Each outlier is ranked by severity—low, medium, or high—so agencies can take swift, informed action where it matters most. For example, during a recent pilot across 60 randomlyselected intersections, the system detected 65 anomalies over one week. Most were low severity, but a few high-severity cases stood out—such as an intersection in downtown Indianapolis impacted by a multi-day freeway closure. The SEPTEMBER 2025
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AI in practice – beyond t Performance in practice
The AI winds are still blowing strong, and everybody is looking to catch a wave, the ITS sector very much included. It is a good time to take stock: is the industry getting up on the surfboard, and how far will it carry us? Here are some practical experiences of AI integration, as well as results from real-world deployments of SmartAI, the integrated traffic control approach from SWARCO, as well as the potential we can look forward to reaping in the months and years to come.
The potential of applying AI in traffic intersection control was clear even before the very first pilot of SmartAI on the SWARCO ITC-3 Traffic Light Controller was installed. By introducing a new paradigm to intersection control, traffic can be managed more efficiently and quickly. The performance potentials can roughly be divided into three categories •
Many bits of AI When introducing AI in traffic control, it’s important to keep in mind that it’s not about making a ‘traffic-GPT’ that you ask to run the traffic logic. Different AI techniques can be useful in many parts of the system. In the SmartAI system, clustering approaches are used for auto-configuring the intersection topology based on detection data, without the need for human to draw lanes on the map. Machine learning is used in the decision process itself, and reinforcement learning is used for predicting the incoming traffic flow that is outside the view of the detectors. So, while the goal of traffic light controller development has not changed at SWARCO for years – to make traffic light controllers that deliver high performance, availability, safety and security, but at the same time are easy to use and configure – the tools and building blocks to achieve these goals are changing. SEPTEMBER 2025
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Here-and-now improvements. We see these in intersections that are struggling under their current control. Even with a well-optimised vehicle actuation and an adaptive system on top, if queues are building up during rush hour traffic, we always expect to see an improvement Event-induced improvements. A nightmare scenario for most traffic control is unforeseen events that have high impact on the traffic flow. An accident might block a main road and send all vehicles down side streets. Traditional plan-based VA breaks down completely in this situation, and while traditionaladaptive approaches will get the picture eventually, the queues will already have built up greatly. SmartAI has no concept of main or side directions, it just handles the traffic that appears at the intersection. Over-time effects are due to longer term changes in traffic characteristics. City planning and overall increase in traffic means that previously well-designed VA control is starting to suffer, and the usual action – to undertake an ITSInternational.com
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After applying SmartAI to the existing ITC-3 Traffic Light Controller, the improvement was instantly noticeable and measurable.
BEFORE
AFTER
As we can see, the peak is both smaller and shorter, indicating clearly that the algorithm is able to handle the traffic efficiently for longer as the pressure ramps up. And, once queues become unavoidable, SmartAI manages to keep them noticeably shorter, with a peak travel time of less than 200 seconds. On some days it even manages to avoid the queues altogether, and it is hardly possible to see the morning rush in the travel time measurement. These measurements stem from two consecutive weeks in October 2024, spanning the same week days (Wed-Fri) for best possible comparison.
What to expect? As can be plainly seen, there is a very large performance potential to be reaped. In fact, the first five pilots of SmartAI, undertaken during 2024 and evaluated very closely, managed an average rush-hour delay reduction of 11%. Subsequent installations show the same trend. They also tell us something about performance expectations. In terms of here-and-now performance gains, it is possible to apply some rule-of-thumb predictions on the magnitude of the improvement. Firstly, it’s important to assume that the existing configuration of the intersection is well-suited for the job. After all, delivering a performance increase on 10-year-old configurations that are nowhere near suited for the experienced traffic situation, can always be achieved. So, any fair evaluation must start from the best performance the existing technology can muster. After that, one can look at the algorithm’s ‘room to manoeuvre’. More lanes, protected turns and stage combinations are indicators that the algorithm will be able to deliver a greater performance right away. In other words, the more complex the intersection, and the more traffic increases, the further AI control pulls ahead of the traditional method of a traffic engineer to precalculate and predict how to run the intersection most optimally. There is another important aspect that should not be forgotten. A traditional vehicle actuated control can be clearly described in a signal group diagram. The AI control depends on more than just detection zones becoming active and actually finds the best performance control with respect to the traffic in real-time, making it much harder to describe exactly the decision it will take ahead of time. This can feel uncomfortable as a road owner, who has to transition from fully understanding what the logic does, to having to trust it. At SWARCO, it is imperative to build this trust with the SmartAI system, so considerable effort has been spent on visualisation of the measurement and values that go into the decision-making process. That makes it possible to see, in real time, the choices that the algorithm makes, as well as the underlying reasons why it made those choices. This inspires confidence that we can in fact trust the system to control the flow of traffic.
the potential expensive redesign of the VA – is usually not triggered until the suffering has been going on for some time. With SmartAI, there are no previous expectations of traffic coming from humans, the system ‘sees’ what is there and keeps controlling in an optimal fashion for years. It should be mentioned that AI intersection control has not been around long enough to provide any actual real world measurements of the over-time effects. These have been proven through simulation. Showing event responses in the real world is a game of patience, as technical director Anders Palm from SWARCO TECHNOLOGY in Odense, Denmark explains: “Sudden and drastic changes to the traffic flow are fairly few and far between. As we move the installation numbers up in the hundreds and thousands, the law of big numbers will help us study these more closely in practice.” He adds “Closing a main artery of a city to see the response of the traffic control would be incredibly interesting from an academic viewpoint but, not surprisingly, is not a popular exercise to do.” But the here-and-now effects are, as the name implies, instant. Here is one result from Ishøj, Denmark. It involves an intersection dealing with a heavy morning rush, moving people between a motorway and city centre from several directions. The alreadyoptimised vehicle-actuated control was seeing travel times through the intersection of up to 300 seconds (compared to a free-flow movement of about 20-30 seconds). Fortunately, the road users were not prepared to just accept the delays. SEPTEMBER 2025
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Innovative Traffic Management Solutions:
Tattile’s Licence Plate Recognition and Vehicle Classification Technologies Since its establishment in 1988, Tattile has been a leading player in the ITS sector, providing high-quality licence plate recognition (LPR) cameras and sophisticated software solutions for various mobility applications. Now part of the TKH Group, Tattile continues to push the boundaries of innovation, offering reliable and efficient products that meet the evolving needs of modern transport systems. What sets Tattile apart, the company says, is the complete in-house development of both hardware and software, resulting in optimised performance. The hardware architecture is designed to maximise the software performance. At the same time, the software is developed to exploit the hardware architecture to its highest potential. Tattile’s systems are built to operate with real-time and high-speed recognition, achieving up to six transits per second. This is critical for applications where traffic volume is high and quick data collection is essential, such as tolling systems, border crossings and law enforcement. At the core of Tattile’s offering is Stark, the company’s powerful software platform. This intuitive solution is designed to offer maximum flexibility, allowing users to easily configure and manage Tattile’s ALPR cameras. Stark is compliant with strict cybersecurity standards, including IEC62443 certification, and it can be integrated directly into the cameras, hosted on-premises or in the cloud. Tattile says that one of its major strengths is its neural Optical Character Recognition (OCR) technology, which is embedded and developed to reach the best fit in Stark cameras. By leveraging advanced neural networks, Tattile has achieved a licence plate reading accuracy of up to 98.5%. This ensures that the system can handle complex licence plates with varying fonts, designs or colours, while still delivering reliable results. Beyond licence plate recognition, Tattile’s systems are capable of detecting and interpreting Accord Dangerous Routier (ADR) plates and identifying vehicles transporting dangerous goods, which enhances overall safety and compliance monitoring. The company offers OCR algorithms for worldwide coverage. In addition, Tattile has expanded its capabilities by launching Stark OCR Cloud, a cloud-based service that extends the reach of its embedded OCR technology.
SEPTEMBER 2025
Stark OCR Cloud can be used as a second-level solution when combined with Tattile’s cameras, providing enhanced processing and analysis capabilities. Alternatively, it can function as a first-level solution with all other types of cameras, offering a complete OCR solution. This cutting-edge recognition software is developed using different technologies and trained on a specific dataset collected from Tattile’s cameras and other devices. It is resolution independent, so it can work with Tattile or third-party camera systems, offering a scalable solution for organisations seeking to improve their existing infrastructure. By harnessing the power of the cloud, Stark OCR Cloud ensures that the technology can meet the needs of any deployment, improving overall efficiency and performance. Furthermore, Stark OCR Cloud provides the Tolling Automation features, which entirely automate the manual review decision, with 99.9% confidence in correct reading. Tattile’s product range now comprises laser-based sensors for vehicle measurement and classification that use AI algorithms. This integration of technologies creates new opportunities to further optimise traffic management and toll collection, offering customers a full suite of high-performance, AI-driven solutions for various mobility applications. Tattile’s portfolio is strengthened by providing advanced tolling and traffic flow optimisation solutions, with technologies designed to measure vehicle volume and classify vehicles based on size. By combining these capabilities with Tattile’s licence plate recognition systems, the two technologies offer a comprehensive solution that not only increases operational efficiency, but also reduces traffic congestion and environmental impact. In conclusion, Tattile’s advanced licence plate recognition cameras, combined with laser-based vehicle measurement solutions, provide a complete, integrated system for traffic management. Together, these technologies offer a more efficient way of handling tolling, vehicle classification and real-time monitoring, making transportation systems smarter, safer and more sustainable. With its focus on innovation and artificial intelligence, Tattile is well-positioned to lead the way in transforming the future of mobility. Find out more at www.tattile.com
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How AI is opening doors… and bus gates…in modern traffic control applications As cities evolve into smarter, more connected transport ecosystems, the need for accurate and reliable vehicle detection becomes increasingly critical. From adaptive traffic signals to real-time congestion management, transportation authorities rely on sensor technologies to make data-driven decisions. For decades, transport authorities have relied on two common forms of detection, inductive loops and aboveground detection in the form of Doppler or FMCW radar. However, with the advent of Artificial Intelligence, a wave of new optical-based detectors is rapidly becoming the standard of modern ITS infrastructure. This technological shift is in response to a growing need for systems that understand how the road space is being used and using this data to make more informed traffic control decisions.
to use the bus gate for layovers, which can trigger false demands. The combination of these three factors presents a challenge for conventional detection methods, which is where the accuracy of AI makes a real difference. With just a single above-ground detection unit, both the bus classification zone and presence zone can be monitored, reducing complexity and implementation costs. Where conventional loops would have to be carefully placed, or radar detectors configured in a rudimental way, the AGD650 simplifies set-up through its web-based user interface. Through a tablet or PC, precise detection zones can be easily defined, tested and adjusted with a direct Wi-Fi connection to the device, ensuring the signalling logic performs optimally in a matter of minutes. By strategically placing the classification and presence zones, along with introducing a delay to prevent layover buses from triggering false demands, the AGD650 can meet the needs of bus gate installations where maintaining efficiency and improving public transport are a top priority. This is just one of the many examples that demonstrate how AI-driven detection technology can be effectively deployed to support intelligent transport infrastructure. The successful collaboration between local authorities, consultants and technology providers ensures that implementations such as these are accurate, efficient and deliver real value for the travelling public. The AGD650, with its AI-based classification and edge processing powers make it ideal for smart mobility solutions that require accurate vehicle discrimination and presence detection. For more information about AGD650 and other products, please visit agd-systems.com
AI-enabled bus gates A key component of the bus gate implementation is the use of AI-based vehicle detection to differentiate between buses and other vehicles. The AGD650, an optical detection system, can be used to provide accurate classification and presence detection. The AGD650 multimodal detector is a camera-based device that features on-board edge processing capabilities, a benefit that allows it to be easily integrated into both modern and legacy traffic systems. In a typical bus gate scenario, two types of vehicle detection are required. Firstly, the bus classification zone, to detect and classify approaching buses entering the bus gate, and secondly, the presence zone, to detect the presence of any vehicle near the stop line to manage signal demand logic. Additionally, it is not uncommon for buses SEPTEMBER 2025
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AI Traffic Data System
AI-powered traffic reporting. iTHEIA™ data recording. Automated Enforcement
Safer and smarter roads. Fully-automated solutions. Weigh-in-Motion
Accurate WIM tech. Efficient traffic monitoring. Multi-Lane Free Flow Toll Migration Improved operational performance.
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iTHEIA: From Roadway Data to Tolling Solutions iTHEIA bridges the gap between advanced traffic data analysis and effective tolling solutions, empowering smarter, more efficient transportation systems. In the rapidly evolving landscape of urban development and traffic management, the need for accurate, reliable, and efficient traffic data collection has never been more pressing. Utilizing edge computing, video, and machine learning, iTHEIA is revolutionising the way traffic data is collected and analysed. At its core, iTHEIA leverages high-performance AI hardware to perform vehicle counting and classification. Unlike traditional methods that may rely on third-party processing, iTHEIA operates independently, processing data directly from video at the roadside. This innovative approach not only enhances data security but also improves the speed and accuracy of traffic analysis.
Permanent or Portable iTHEIA offers two configurations: permanent and portable. The permanent version is built for long-term use, featuring durable components to withstand extreme environmental conditions and ensuring reliable traffic monitoring 24/7. The portable version, on the other hand, offers flexibility for short-term studies or periodic traffic assessments. Both versions include the iTHEIA web interface, which provides real-time video feeds, vehicle analytics, and customisable lane configurations.
Applications in Tolling Using iTHEIA for vehicle classification in toll systems represents a significant advancement in automated toll technology. Currently being trialled in Illinois with the Illinois State Toll Highway Authority (ISTHA), iTHEIA has demonstrated exceptional performance in vehicle classification, particularly for axle counting applications, Quarterhill says. Accuracy surpasses that of the legacy in-road sensors. During the trial phase, iTHEIA is used for data auditing purposes, serving as a robust tool to compare its results against traditional toll data collected daily. The system’s flexibility enables it to handle complex scenarios, such as detecting motorcycles straddling lanes or vehicles driving on shoulders—instances where traditional sensors often fail. Beyond tolling, iTHEIA holds potential for predictive maintenance by identifying discrepancies between camera data and in-road sensors, as well as for applications like congestion pricing.
Real-world Application and Experience iTHEIA’s impact is evident through its successful deployments across the United States. In North Dakota, 22 permanent iTHEIA systems are operational and fully integrated into the state’s enterprise data reporting system. Agencies in Wisconsin, Mississippi, Oklahoma and Pennsylvania have validated iTHEIA’s performance against FHWA reporting criteria. In September, the Minnesota DOT announced the acquisition of 15 additional systems, highlighting the impressive performance of the portable version. Expanding its reach internationally, iTHEIA’s portable units are now being used for traffic studies in Mexico, marking the system’s first application outside the US.
Conclusion
Innovative Features
The iTHEIA AI Traffic Data System is a major innovation in traffic monitoring. Its advanced AI capabilities, combined with its flexibility and user-friendly design, set it apart in the traffic data industry. With recent enhancements and strong user feedback, iTHEIA is not only addressing the needs of today’s traffic professionals but is also well-positioned to tackle future challenges, including potential applications in tolling systems to optimise traffic flow and revenue collection.
Quarterhill constantly enhances iTHEIA’s capabilities to meet the growing demands of modern traffic monitoring. The permanent iTHEIA system now supports one or two cameras, allowing coverage of up to 10 lanes without compromising data accuracy. The ‘Auto-learn lanes’ feature simplifies set-up by automatically identifying lane configurations and traffic flow patterns, cutting down on deployment time and reducing manual errors. The portable model has also been upgraded with a quick-disconnect mechanism for its rechargeable lithium battery, allowing field teams to conduct multiple traffic studies efficiently. SEPTEMBER 2025
Find out more at www.quarterhill.com/solutions/videobased-systems
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Smart City ready with vehicle, bicycle, and pedestrian detection High performance video detection
Autoscope® OptiVu is taking video detection to new heights by offering smart object detection algorithms powered by AI and machine learning. The camera and processor provide high performance vehicle detection, bicycle and pedestrian detection, enhanced traffic data collection in real-time, and Smart City readiness with connectivity to other systems. Maximize your Autoscope OptiVu detection system with Centracs® +Detect, our revolutionary cloud-based data analytics, monitoring, and reporting platform. +Detect supports informed traffic management decision making and signal optimization.
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The Critical Role of AI in
Next-Generation Sensors for Dynamic Intelligent Traffic Systems Data is the vital fuel that powers the decision-making and operational management engine of ITS and Smart City ecosystems. Today’s roadway and intersection sensors are delivering new levels and volumes of roadway data. But raw data alone is not enough. The true power of these sensors is unlocked when sensors are paired with AI, transforming passive observation into active understanding and situational awareness. In the case of emerging video sensors, AI doesn’t just enhance visual acuity – it helps revolutionise the optimisation strategies and capabilities for Smart Cities. For the rapidly evolving landscape of transportation management, AI has emerged as a cornerstone technology. This is evident in next-generation video detection sensor platforms that are leveraging AI to help deliver real-time data and performance necessary for the situational awareness, image enhancement, vehicle classification, object tracking, and Vulnerable Road User (VRU) detection required for Dynamic Multimodal Network Management (DMNM).
AI-enhanced field of view detects, tracks, and identifies objects in complex environments with remarkable accuracy.
false positives or missed alerts. This is especially important for automatically triggering safety-critical applications like pedestrianvehicle conflict mitigation, dilemma zone protection, and safety countermeasures. This level of situational awareness opens new proactive operational possibilities for transportation agencies and Smart Cities. AI enables video sensor solutions to deliver precise vehicle and object classification by analysing shape, size and motion patterns, which are essential to differentiate between cars, trucks, buses, bicycles and VRUs in real time. This dynamic capability is vital for dynamic traffic signal control, as well as traffic analytics and infrastructure planning, supporting mobility and Vision Zero goals. VRU detection is one of the most challenging yet critical tasks. AI-powered video sensors can detect pedestrians in real-time with high precision, even in complex urban environments. This data is crucial for future active mobility user programs in support of sustainability and inclusivity initiatives.
From Passive to Proactive Until recently, video detection sensors captured and transmitted footage, often leaving the task of interpretation to human operators or post-processing systems. Additionally, video sensor accuracy was significantly affected by lighting and weather conditions, while the roadway data was not dynamic. AI is changing the game by leveraging deep machine learning to enhance accuracy, as well as supporting real-time analysis and decision-making at the edge or in the cloud. AI algorithms are helping video sensors deliver real-time object detection, instantly identifying VRUs, classifying vehicles and tracking objects. This, in turn, is providing the roadway data that can recognise patterns, feeding predictive strategies and applications. The shift from passive monitoring to proactive intelligence is especially vital in continually optimising for safe, sustainable, and accessible mobility.
Situational Awareness: Seeing the Whole Picture
The New View of AI in Transportation
To fulfil the promise of proactive intelligence, next-generation video sensors must be designed to provide real-time, HighDefinition (HD) video detection that supports DMNM. By integrating AI-powered detection algorithms and deep machine learning, video sensors like Autoscope OptiVu by Econolite can interpret complex traffic environments with remarkable accuracy. This includes identifying and tracking vehicles and VRUs across intersections and corridors. Deep learning is helping to upscale video resolution, reduce noise, and enhance clarity. These enhancements are crucial, where visual fidelity directly impacts detection accuracy and data resolution. For instance, a blurry image of a pedestrian at night can be clarified to ensure accurate detection, preventing SEPTEMBER 2025
AI is not just enhancing video sensors; it’s redefining what’s possible for ITS. From the granular detection of a single pedestrian to the orchestration of multimodal traffic flows, AI-powered video sensor platforms are enabling cities to be safer, smarter and more proactive. A key moving forward will be leveraging and integrating AI-powered sensors with detection system data analytics and reporting platforms. This facilitates the application of advanced analytics, remote monitoring and predictive modeling—all essential for emerging mobility applications. To learn more about Econolite’s smart sensor solutions, visit www.econolite.com/solutions/traffic-sensors/
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A I I N T R A N S P O RTAT I O N
Navigating the future of Intelligent Transportation with the MAV AiQ
of accuracy and confidence, the end results can become almost unbeatable. Such intelligence is central to broader ITS goals. By continuously refining its recognition capabilities, MAV AiQ supports accurate enforcement of motoring traffic offences such as speeding, illegal lane usage, and red-light running. The system’s real-time integration with enforcement networks allows for immediate action, helping to deter dangerous behaviour and reducing accident rates. Moreover, this data-rich platform contributes to public safety beyond routine enforcement. By integrating with emergency alert systems, such as Amber Alerts, the AiQ provides authorities with vital tools for rapid vehicle identification and incident response, enabling faster resolutions in critical situations.
In the face of accelerating urbanisation, environmental urgency and evolving mobility patterns, today’s Intelligent Transportation Systems (ITS) must do more than simply manage traffic. They must anticipate, adapt, and act intelligently. The challenges are diverse: congestion in mega-cities, ever-evolving criminal tactics, ageing infrastructure and the increasing demands for real-time data integration. As these pressures mount, the role of AI in transportation becomes not just beneficial, but fundamental. Among the most transformative AI-driven tools emerging at the forefront of ITS is ANPR technology. Far from its roots in basic vehicle identification, modern ANPR is reshaping how cities enforce laws, manage congestion and plan for the future. Nowhere is this evolution more evident than in the MAV AiQ, an AI-powered platform that embodies the next generation of traffic intelligence.
Data-Driven Planning for Tomorrow’s Cities Urban mobility is no longer about simply moving vehicles, it’s about managing complexity. The MAV AiQ is designed not only to observe but to generate actionable insights. Through high-resolution capture and AI-based interpretation, it provides granular data on vehicle behaviour, compliance trends and traffic flow patterns. For urban planners, this translates into smarter infrastructure development, optimised signal timing and informed long-term investments. For law enforcement, it offers enhanced forensic capabilities. And for traffic operators, it presents a new level of precision in managing congestion and emissions. Critically, the MAV AiQ’s modular architecture ensures scalability for Smart City ambitions. It enables the creation of intelligent, interconnected camera networks that feed into centralised management systems, which support integrated strategies for sustainability, safety, and efficiency.
From Passive Observation to Intelligent Action Traditional traffic enforcement systems were largely static, reactive, and reliant on manual oversight; AI and the MAV AiQ, however, change the paradigm. Integrating Edge Technology within existing and new traffic system platforms allows for autonomous decision-making, realtime adaptation and continuous learning, capabilities that are essential in today’s dynamic traffic environments. This leap in intelligence is not just technical but practical. The MAV AiQ incorporates advanced technologies such as GhostPlate, AI Accurate, and PlatePath, proprietary systems that are designed to counter increasingly sophisticated attempts at licence plate tampering. Ghost plates, which are designed to be invisible to conventional systems, are now a growing concern in enforcement circles. The MAV AiQ’s ability to detect and respond to these and other plate manipulation methods underscores its critical role in maintaining lawful compliance on the roads.
Part of a Bigger Vision MAV Systems is part of the growing collaboration of traffic technology businesses within The Traffic Group, which further strengthens technological innovation. Together with AGD Systems, a leader in radar detection and traffic sensors, and TGS, known for its portable enforcement solutions, The Traffic Group leverages AI across its portfolio to deliver the most accurate and high-quality traffic products on the market. This holistic application of AI, from fixed infrastructure to mobile and environmental sensing, supports a unified approach to safer, smarter roads. Find out more at www.anprcameras.com
AI at the Heart of Smarter Streets At its core, AI brings resilience and adaptability. MAV AiQ’s machine learning algorithms improve recognition accuracy over time, automatically adjusting to variables such as weather, plate design changes and real-world interference. This ensures consistent performance without the need for ongoing manual recalibration, something legacy systems cannot offer and, when starting from such a high level SSEEPPTTEEM MBBEERR 22002245
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