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AI Empowering Smarter, Safer and More Sustainable Traffic
By Wang Jun, General Manager of ITS Product R&D at Dahua Technology
Artificial intelligence is transforming traffic management from passive monitoring to intelligent decision-making. At Intertraffic Amsterdam 2026, Dahua Technology shared its perspective on how AI and large models are reshaping the future of mobility.
As urbanization accelerates and mobility demand continues to grow, cities around the world are facing increasing pressure on their transportation systems. Traffic congestion, road safety risks, and operational complexity remain persistent challenges for traffic authorities.
Artificial intelligence is emerging as a critical technology to address these issues. By enabling transportation systems to perceive, analyze, and respond to road conditions more intelligently, AI is helping reshape traffic management and laying the foundation for safer, more efficient, and more sustainable mobility.
From Visibility to Intelligence
The evolution of intelligent traffic systems can be broadly understood in three stages: seeing, seeing clearly, and understanding.
Early traffic monitoring systems mainly focused on providing visibility of road conditions. With advances in imaging technologies and sensing devices, traffic cameras gradually became capable of capturing clearer data and operating reliably in complex environments such as low light or adverse weather.
The introduction of AI and deep learning technologies marked the next major shift. Instead of simply recording traffic events, intelligent systems began to recognize vehicles, identify violations, and analyze traffic behavior with significantly higher accuracy.
Today, traffic management is entering a new phase where systems can interpret complex traffic scenarios and support intelligent decision-making, moving beyond passive monitoring toward proactive traffic operations.
The Role of Large-scale AI Models
Recent advances in large-scale AI models are further accelerating this transformation.
By integrating visual perception, multimodal sensing, and semantic understanding, large models enable traffic systems to interpret complex environments with greater contextual awareness. At Dahua, this approach combines vision-based analysis, radar–video fusion, and language-based interaction capabilities to support a more intelligent traffic management framework.
Together, these technologies enable a closed-loop operational model connecting perception, analysis, decision-making, and control. In practical deployments, traffic systems can dynamically detect congestion, accidents, and violations, generate response strategies, and optimize traffic signal operations in real time.
Wang Jun, General Manager of ITS Product R&D at Dahua Technology, delivers a keynote speech at Intertraffic Amsterdam 2026
Improving Reliability in Complex Scenarios
Large AI models also demonstrate clear advantages in handling complex real-world traffic situations.
For example, detecting driver behaviors such as mobile phone use or seatbelt violations can be challenging due to shadows, reflections, or visual similarities inside the vehicle. With improved contextual understanding, AI systems can significantly reduce false alarms while increasing detection accuracy.
Similarly, AI-powered traffic systems are improving incident detection on highways and urban roads. By combining multimodal perception and contextual analysis, these systems can identify a wider range of incidents—including collisions, rollovers, and vehicle fires—allowing authorities to respond more quickly and effectively.
Simplifying Deployment and Operations
Another important benefit of AI lies in simplifying the deployment and operation of traffic systems.
Traditionally, configuring traffic enforcement devices required extensive manual setup and professional expertise. AI-powered scene recognition now allows cameras to automatically identify traffic environments and calibrate detection zones within seconds, significantly improving deployment efficiency and reducing maintenance costs.
AI is also transforming traffic signal management. Intelligent signal control agents can analyze congestion patterns, diagnose traffic issues, and generate optimized signal timing strategies. Through more intuitive interaction methods, traffic operators can manage complex intersections more efficiently and continuously improve traffic flow.
Toward an Integrated Intelligent Traffic Ecosystem
Looking ahead, the future of transportation will rely on integrated intelligent ecosystems that connect multiple traffic scenarios.
AI-enabled technologies are already supporting a wide range of applications—from urban traffic management and adaptive signal control to highway monitoring, incident detection, and smart parking solutions. Together, these capabilities help create a more comprehensive and responsive traffic management framework.
Advancing the Future of Intelligent Mobility
As cities continue to evolve, transportation systems must become increasingly adaptive, data-driven, and resilient.
Artificial intelligence is enabling traffic management to move beyond passive monitoring toward proactive and predictive operations, helping cities optimize road resources, improve safety, and enhance travel efficiency.
At Dahua Technology, ongoing innovation in AI, sensing technologies, and system integration remains central to advancing intelligent mobility. Through collaboration with global partners, we aim to support the development of transportation networks that are safer, more efficient, and more sustainable for cities worldwide.
