Traffic Technology International
Field-proven AI experience with decades of data separates reliable traffic detection from untested promises
The integration of artificial intelligence from novelty t(AI) into traffic management is evolving to necessity an evolution driven by years of experience and a deep understanding of traffic dynamics. While a new wave of companies has recently entered the AI space, true accuracy and reliability lies with those who have been pioneering this next- generation technology for decades. A short history reveals a clear distinction between fleeting hype and enduring expertise.
In the late 1990s, the concept of video-based automatic incident detection was revolutionary. It was an early form of machine vision, a precursor to the deep learning models used today by companies like Citilog. This pioneering research laid the groundwork for sophisticated AI that can analyze traffic in real time and detect a wide range of anomalies, from stalled vehicles to wrong-way drivers, reducing response times and potentially saving lives.
Citilog, in particular, has deployed its AI technology on more than 60,000 cameras across more than 1,600 global implementations. This vast, real-world experience, integrated into its deep learning model since 2019, is a critical advantage that newcomers simply cannot replicate in their solutions.
This foundation of field-proven data directly addresses a major weakness in many newer AI systems: the problem of false positives. Without a rich, real-world training dataset, algorithms can struggle with common environmental factors like shadows, glare, or sudden weather changes. This leads to a high rate of spurious alarms, forcing operators to spend valuable time filtering out non-events and eroding confidence in the system. A mature AI platform with deep learning, however, is trained on years of diverse traffic scenarios, allowing it to differentiate between a shadow and a pedestrian, or a cloud of exhaust and a fire, reducing false alarms by a factor of ten.
In an emergency, the speed and accuracy of an AI system, especially one fine-tuned for high-risk environments, can make the difference between a routine response and a full-scale tragedy
1. Wrong way driving is one of the many incidents automatically detected by AI
2. Citilog’s AI uses deep learning to classify types of vehicles and VRUS, while reducing false positives by a factor of 10
AI for all environments
This long-term approach is critical in complex and high-stakes environments like intersections, highways, bridges, and tunnels. These spaces present unique challenges, including high-speed traffic, bottlenecks, and potential conflict points between vehicles and pedestrians or other vulnerable road users. A general-purpose AI model would not suffice. A solution specifically trained on these conditions, however, can detect unique events like a cyclist in a restricted zone or even smoke in a tunnel.
The deployment of these advanced solutions is also a key differentiator. While many AI systems require central servers, Citilog’s latest video detection algorithms can be deployed either on servers or directly on AI-enabled cameras to leverage advanced edge processing. This advantage dramatically reduces latency and bandwidth requirements, enabling real-time signal actuation and the much faster detection of traffic incidents.
60,000 The number of cameras deployed worldwide with Citilog’s AI tech
3. Citilog’s AI detects incidents like tunnel fires early, which is critical to a swift response by
emergency services
The Vuache Tunnel incident
A recent incident in the Vuache Tunnel in the French Alps offers a powerful example of why experienced AI technology and speed of detection is critical. In June this year, a heavy goods vehicle laden with plastic pellets came to a stop in the southbound section. At 10:33, the tunnel’s automatic incident detection system from Citilog immediately identified the stopped vehicle and alerted the control center.
The tunnel’s officials quickly activated closure procedures, preventing any other vehicles from entering. Just five minutes later, at 10:38, a fire broke out on the truck. Thanks to Citilog’s early warning, the tunnel was already closed, the driver was safely evacuated, and no other vehicles were involved. This incident demonstrates that in an emergency, the speed and accuracy of an AI system, especially one fine-tuned for high-risk environments, can make the difference between a routine response and a full-scale tragedy. This level of performance is a testament to the power of a robust and mature AI platform.
Planning for the future
The future of AI in traffic management rests on proven, resilient systems, as agencies recognize that novelty alone is insufficient to ensure long-term safety and efficiency. Citilog contributes to that future by refining methods that have stood the test of decades, demonstrating that real innovation in AI traffic detection comes from experience, continuous learning and improvement, and repeated exposure to complex real-world conditions.
