Urban congestion is one of the defining struggles of modern cities. Drivers spend time stuck in traffic, public transport loses reliability, freight and deliveries slow down. Cities pay for all this lost productivity and higher emissions. Smart traffic lights are emerging as a practical tool to change that. They combine sensors, connectivity, and artificial intelligence to adapt traffic control in real time. This article explains how they work, looks at European examples, and asks whether the Balkans could benefit from similar systems.
AI-driven traffic management explained
Smart traffic lights go beyond fixed schedules. While traditional signals follow pre-set cycles and do not adapt to unexpected changes, smart lights use data to make decisions. Vehicles, cyclists, and pedestrians are detected and recorded. Cameras, inductive loops, radar, and connected vehicle signals feed the system. That data is processed locally at intersections or centrally in traffic control centers. Machine learning models analyze flows and predict short-term demand and the lights are then adjusted – phase lengths and offsets are adapted to reduce delays.
One common method is adaptive signal control. The system shortens green times on streets with low demand and lengthens them where queues build up. Another method optimizes coordination along corridors. Green waves are created dynamically so platoons of vehicles pass through multiple intersections without stopping. AI also helps prioritize public transport and emergency vehicles, while buses can get extended greens.
Smart signals also reduce uncertainty in urban traffic systems. They detect incidents early, like sudden slowdowns or blocked lanes. AI can suggest or automatically implement reroutes. When combined with variable message signs and journey time forecasting, these systems help drivers and fleet operators make better choices. Data from smart signals feeds mobility-as-a-service platforms and navigation apps. This makes routing more accurate and fairer across the network.
Privacy and data security are also important, so modern systems use anonymized or aggregated data. Cities must set clear governance rules during implementation. Open standards and interoperability matter too. Signals that can talk to different vendors and to vehicles create more value. That is why many cities adopt standards such as ITS-G5, NTCIP, or SAE C-V2X for communication.
Case studies from Europe
Several European cities have deployed smart traffic lights with measurable benefits. Below are short case studies that show a range of outcomes and approaches.
Barcelona, Spain
Barcelona pursued a smart city strategy focused on mobility and quality of life. The city installed sensors across many intersections. A central traffic management platform runs adaptive algorithms. Results included reduced travel times on key corridors. Bus punctuality improved. Pollution hotspots were also better managed because idling decreased. Barcelona combined traffic signals with parking and micro-mobility data to create a more integrated system.
London, United Kingdom
London uses a mix of fixed and adaptive control across its vast network. Transport for London (TfL) deploys SCOOT and MOVA systems at many junctions. These systems react to real-time demand and vehicle arrivals. TfL has also trialed AI-based controllers to optimize signals based on predictive models. The focus in London is on balancing vehicle flow with pedestrian safety and public transport priority. Results include modest travel time savings and improved bus reliability on targeted routes.
Amsterdam, Netherlands
Amsterdam emphasizes sustainable mobility. Traffic lights there prioritize cyclists and public transport in many areas. The city uses dynamic coordination to create green waves for bicycles during rush hours. Smart infrastructure integrates with bike counters and sensor networks. The approach led to smoother cycling flows and reduced conflicts between modes. Amsterdam’s model shows that smart signals can support modal shift goals, not just car throughput.
Munich, Germany
Munich implemented an intelligent traffic management system that links signal control with public transport schedules and incident detection. The system uses machine learning to forecast demand and optimize signal timing. Trials showed reduced delays and improved network resilience during special events and roadworks. Munich also uses simulation tools to plan signal changes before deployment.
Lisbon, Portugal
Lisbon focused on data-driven optimization across corridors connecting suburbs to the city center. A combination of local adaptive controllers and corridor-level coordination reduced stop-start behavior and smoothed traffic. The city also invested in better real-time monitoring, which improved incident response times and informed longer-term planning.
These examples show diverse strategies. Some cities emphasize public transport and cycling. Others focus on corridor throughput or incident management. The common theme is that data, connectivity, and adaptive algorithms deliver measurable benefits when governance and maintenance are in place.
Quantified benefits
Studies and pilot projects across Europe report benefits that include:
- Reduced travel time on targeted corridors, often between 10% and 30%.
- Lower vehicle idling, which reduces CO₂ and NOx emissions.
- Improved bus punctuality and reduced passenger waiting times.
- Better handling of peak demand and special events.
The scale of benefits depends on baseline conditions, the quality of implementation, and integration with broader mobility measures.
Could the Balkans benefit from similar systems?
Short answer: yes. The Balkans face traffic challenges that smart signals could help solve. Many cities in the region have rising car ownership. Public transport sometimes struggles with reliability. Urban cores and key corridors suffer congestion during peaks. Smart traffic lights can offer a relatively fast way to improve flows without building new roads.
Where benefits would be biggest
Corridors with mixed traffic and frequent bus routes are prime candidates. Intersections near transit hubs, freight routes, and busy crossings also stand to gain. Cities with sparse real-time traffic data can leapfrog older systems by installing connected sensors and adaptive controllers. Even incremental upgrades—like giving priority to buses at critical junctions—can improve public transport attractiveness.
Practical considerations
Budget and institutional capacity matter. Smart signals require investment in hardware, software, and maintenance, so cities must plan for long-term operation costs. Training for traffic engineers is essential. Partnerships with universities, local tech firms, and vendors can reduce risk and build local know-how. In order to achieve this, pilot projects are a wise first step. Start small on a few corridors, measure outcomes, and scale successful approaches.
Interoperability is crucial in regions with mixed vendor landscapes. Choosing open standards and modular systems avoids vendor lock-in. Also, clear communication about benefits helps with public acceptance.
Integration with regional priorities
Smart traffic lights should not be isolated projects. They work best when integrated with public transport upgrades, parking management, and active mobility infrastructure. For the Balkans, combining signal priority for buses with improved bus lanes would produce larger benefits than signals alone. Freight logistics can benefit from time-of-day coordination and delivery-window management powered by signal data.
Low-cost options
Not every city needs a full AI-driven control center. Lower-cost options exist. Simple sensor upgrades and rule-based adaptive controllers can yield quick wins. Using existing telecom networks and cloud platforms can reduce upfront infrastructure costs. Open-source traffic management platforms and regional collaboration can further lower barriers.
Conclusion
Smart traffic lights are a practical and proven tool to reduce congestion. They make intersections adaptive, responsive, and cooperative. European case studies show travel time, emissions, and reliability benefits. The exact gains depend on context, scale, and how well systems are integrated with other mobility policies. For the Balkans, these technologies offer clear potential. With careful planning, pilots, and a focus on interoperability and public transport priority, cities can capture benefits without excessive cost. Smart signals are not a silver bullet. They are one effective lever in a wider toolbox for cleaner and more efficient urban mobility.
