Have you ever waited at a red light with the street completely empty? For decades, traffic lights have operated on fixed intervals, regardless of whether there was a traffic jam or no vehicle in sight. Smart traffic lights, based on AI and big data, change that logic entirely: they are systems capable of analysing traffic in real time, anticipating demand and autonomously adjusting their cycles so that cities flow better, pollute less and are safer for all road users.
This article covers the following topics and offers a comprehensive infographic:
What are smart traffic lights?
A smart traffic light is a traffic control system that reacts to environmental conditions in real time. Unlike traditional traffic lights, which operate on pre-set fixed cycles, smart traffic lights combine sensors, cameras and artificial intelligence to autonomously adjust their timing according to the actual volume of vehicles, pedestrians or cyclists present at any given moment.
It is one of the key elements of smart cities and, like other applications such as electricity supply or water management, it depends on two fundamental factors:
- Sensor network. They range from simple cameras to radar sensors, all interconnected through IoT (Internet of Things) technologies. It is also expected that, as Car2X communication protocols are implemented, the vehicles will report their position, speed, and direction, both to nearby cars and to traffic signals and systems in the vicinity.
- Artificial intelligence and big data systems. The massive data collected through the sensors is processed by artificial intelligence, which allows management of the current situation and anticipates events such as peak traffic hours or off-peak holidays.
AI for smooth traffic flow
Smart traffic lights look set to become one of the cornerstones of sustainable mobility. Mainly, they will save time and fuel while reducing the environmental pollution in cities.
But what is the current situation of this technology? Well, it seems that significant progress is being made. One example is the work of Aston University in the UK. This is artificial intelligence software that, through machine learning, is capable of analyzing images from traffic cameras in real time and modifying the behavior of traffic lights.
To achieve this, British researchers have created a photorealistic traffic simulator, codenamed Traffic 3D, where AI can be trained without endangering drivers. The simulator generates different traffic conditions, including rainy situations or accidents.
The reinforcement learning used by AI is a bit like human learning: a reward for getting it right, a penalty for mistakes. Thus, every time there are waiting times or a traffic jam, the system receives a penalty. In this way, the AI has been improving its performance and has already been successfully tested on an actual crossing. In this case, instead of obtaining the information from the simulator, it obtains it from the cameras located on site. The AI also adapts to new circumstances, such as an accident.
Until now, many smart traffic light control systems were based on magnetic induction loops. In other words, a wire crosses the roadway and records the number of passing cars. The program counts and reacts to this data. The new model, on the other hand, will allow a more agile and faster reaction, since it can monitor the traffic that is arriving at an intersection, not just the traffic that is passing through a specific point.
Cities with AI-Powered Smart Traffic Lights in 2025
Although Aston University's software is one of the most advanced in this field, the rollout of smart traffic lights has been underway for years. The most widely cited case is Pittsburgh, which has been working with Carnegie Mellon University since 2012 on its Surtrac system: more than 50 AI-regulated intersections that managed to cut time lost in traffic jams by 40% and emissions by 20%, combining cameras, radars and radio frequency devices.
In Europe, Madrid offers one of the most compelling examples. In late 2024, the city council launched a pioneering municipal programme, the first of its kind in Spain, based on the analysis of 510,000 hours of video captured by 56 cameras. The system recognises pedestrians, cyclists and vehicles and adjusts timing dynamically. Around the Metropolitano stadium, traffic lights adapt automatically when large groups of pedestrians are detected on busy matchdays; on Puente de Segovia, they quantify bicycle flow in real time.
At a global scale, Google Green Light is the most widely deployed programme. Launched in 2021, it currently operates in more than 20 cities across 4 continents, including Hamburg, Budapest, Bangalore, Rio de Janeiro and, since January 2025, Santiago de Chile, managing approximately 30 million trips per month. Its key advantage: no new infrastructure required. Cities can roll out improvements in minutes on top of existing systems, with a demonstrated potential to reduce stops by up to 30% and CO₂ emissions by 10% at the intersections where it is deployed.
Other cities in China, Malaysia and India are applying AI-based traffic management solutions, some developed by Alibaba. And traffic is not the only target: in the UK, a company is already using AI technology that monitors crosswalks and modulates traffic lights based on detected pedestrians and their likely behaviour, anticipating whether or not they are about to cross from their movements alone.
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A smart traffic light is a traffic control system that reacts to environmental conditions in real time. Unlike traditional traffic lights, which operate on fixed intervals, smart traffic lights combine sensors, cameras and artificial intelligence to autonomously adjust their cycles according to the actual volume of vehicles, pedestrians or cyclists present at any given moment.
The AI processes data collected by a network of sensors and cameras installed at intersections. Using machine learning algorithms, the system analyses traffic flow in real time, anticipates congestion and adjusts green times to optimise circulation. Some systems also coordinate several adjacent intersections, creating green waves that reduce unnecessary stops.
The main benefits are three: a reduction in time lost in traffic jams, lower pollutant emissions by avoiding unnecessary acceleration and braking, and greater safety for all road users, including pedestrians and cyclists. According to data from the Google Green Light project, its AI-based traffic optimisation system has demonstrated a potential to reduce stops by up to 30% and CO₂ emissions by up to 10% at the intersections where it has been deployed.
The technology is already deployed across several cities worldwide. Madrid launched its programme in late 2024, analysing traffic with AI cameras at locations including the area around the Metropolitano stadium and Puente de Segovia. Santiago de Chile joined the Google Green Light project in 2025 alongside the Ministry of Transport. Globally, Google's programme operates in more than 20 cities across 4 continents, including Hamburg, Budapest, Bangalore and Rio de Janeiro, managing approximately 30 million trips per month.
Google Green Light is an AI-based traffic light optimisation programme that combines Google Maps driving trend data with machine learning to analyse traffic patterns at each intersection. Its main advantage is that it requires no new infrastructure: cities can implement improvements in minutes on top of existing systems. Since its launch in 2021, it has operated in more than 20 cities across 4 continents and has demonstrated a potential to reduce stops by up to 30% and estimated CO₂ emissions by up to 10% at urban intersections.
A traditional traffic light operates on fixed cycles programmed in advance, with no awareness of what is happening on the street at any given moment. A smart traffic light, by contrast, perceives its environment in real time through sensors and AI, adapts its timing to actual demand and can coordinate with nearby traffic lights. The result is traffic management that is more efficient, more sustainable and better suited to the real needs of each intersection.
Yes. In the most advanced systems, the AI is trained in simulated environments before being deployed on real roads, which minimises risk during the development phase. Furthermore, the technology does not replace human oversight: operators at the mobility management centre can intervene and modify programming in real time if any incident is detected. In cities such as Madrid, the system works alongside mobility officers and the Municipal Police as a backup.