The digital transformation of Traffic Control Centres (TCCs) is reshaping how road operators, concessionaires, and their partners manage and optimise road infrastructure. By integrating advanced technologies such as AI, digital twins, and edge computing, TCCs are moving beyond traditional reactive operations to become intelligent, predictive, and collaborative hubs. This will have impacts on people and positions, placing humans at the heart of enhancing safety and operational excellence, and enabling the industry to meet the demands of future mobility.
The evolving role of the traffic control centre
Traffic Control Centres are the backbone of safe and efficient road operations. Traditionally, they have been centralised facilities where operators use SCADA systems and decision-support tools to monitor and manage complex transport infrastructure. With the advent of artificial intelligence and machine learning, TCCs are transforming. They are becoming intelligent, transparent, and collaborative partners capable of real-time decision-making and predictive planning.
Building the digital backbone
The next generation of TCCs will be defined by a unified technological foundation:
- AI and machine learning: For real-time anomaly detection, automation, and advanced analytics. AI empowers TCCs to anticipate incidents, optimise traffic flow, and support autonomous operations.
- IoT sensors and cameras: Continuous live data collection provides granular, up-to-the-second insights, enabling responsive and informed decision-making.
- Digital twins: These virtual models simulate entire road networks, integrating real-time sensor data for accurate city-wide traffic prediction and management.
- Edge computing: Data is processed locally at the source (such as traffic cameras), reducing latency and enabling rapid, automated responses to changing conditions.
- Blockchain and federated learning: Secure, decentralised data sharing protects privacy and ensures authenticity, addressing long-standing concerns in traditional systems.
- Explainable AI (XAI): Ensures transparency and trust by making AI decisions understandable and auditable. Building trust in AI system increases accountability, reduces risk and aids in debugging and improving the system itself.
For road traffic control centres, this means a shift from reactive management to predictive, AI-driven orchestration hubs. Key technologies will include hybrid digital twins for citywide traffic simulation and management, AI for integrating and operating large fleets of Level 4 and 5 autonomous vehicles, and advanced V2X communication to enable dynamic lane and intersection control for efficient mobility.
The evolving role of human operators
As technology advances, the responsibilities of TCC staff will shift. Operators will move from manual, reactive tasks to roles focused on predictive planning, real-time coordination, and oversight of autonomous systems. This evolution supports smarter, safer, and more efficient mobility management. Future roles could include:
An autonomous network supervisor, who oversees the health of Vehicle-to-Everything (V2X) and Intelligent Transportation Systems (ITS) networks, ensuring that adaptive signal algorithms function optimally. The role involves monitoring AI performance and intervening manually in cases of complex system failures. The supervisor would also be responsible for maintaining the integrity of Dedicated Short-Range Communications (DSRC), Communication Vehicle-to-Everything (C-V2X), and Roadside Units (RSUs).
Incident & edge-case manager, who handles complex incidents, accidents, and weather-related edge cases. This person is responsible for executing system overrides, such as updating Variable Message Signs (VMS) and managing lane closures, while coordinating emergency services. The incident & edge-case manager also classifies sensor anomalies and drives recovery strategies to restore normal operations quickly and effectively.
Mobility data & planning analyst, who uses AI forecasts and digital twin simulations for strategic planning and optimisation of traffic management. This person is responsible for running predictive analytics for traffic projections ranging from 4 to 24 hours to support proactive decision-making. They also measure the return on investment (ROI) of automation and refine system parameters, including Mobility-as-a-Service (MaaS) integration.
These roles now focus on predictive, AI-driven orchestration enabling dynamic rerouting, congestion forecasting, and prioritisation for autonomous vehicles.
Road control centres: the future
“We manage 31 infrastructure projects covering 6,898 km of roadway, supported by 15 control centres dedicated to safety and operational excellence. Looking ahead, these centres will evolve from traditional traffic management to predictive mobility solutions, leveraging advanced analytics and real-time intelligence to proactively enhance flow, safety, and sustainability.”- Richard Lengrand, Director, Egis Road Operations
Egis operates a wide range of transport infrastructure, from motorways to airports, rail and parking solutions. This gives us a unique perspective on the innovations that promise to revolutionise decision-making and operations in the future.
Our vision focuses on harnessing advanced technologies, making decisions based on data, and adopting sustainable methods to ensure infrastructure is managed efficiently and safely. We are not simply digitising control centres for the future; we are redefining their very function. Every operational post serves as a node within an intelligent, distributed network powered by real-time data, immersive interfaces, and autonomous systems.
For road operators, concessionaires, and their partners, the digital transformation of TCCs presents an opportunity to enhance safety and efficiency. By embracing these new technologies and evolving operational roles, the industry can meet the demands of modern mobility and deliver even better service to road users.
