When algorithms manage traffic - How artificial intelligence is rethinking our traffic management

The transportation of tomorrow is not only electric or automated - it is above all data-driven. And it needs tools that can understand, evaluate and control complex systems in real time. Artificial intelligence is one of these tools. Now is the time to get to grips with it.

Monday morning, shortly after half past seven. In a medium-sized city, the traffic control center looks at its monitors: dense traffic jams on the feeder road into the city center, an accident is reported via the system, the traffic light control system is reacting slowly. At the same time, the national traffic control center receives an incident report from the freeway section close to the city - another bottleneck is looming there too. The two situations appear to be independent of each other, but they are part of a systemic problem: traffic flows are still mainly controlled reactively - with rigid rules, based on empirical values, often with a time delay. But something is happening. Traffic flows are increasingly not only being observed, but understood - by systems that can learn.

Artificial intelligence (AI) promises to accelerate this change. From rigid rules to learning systems, from management "after the traffic jam" to proactive, predictive control of the entire transport network - both locally and nationally. AI is not just a new IT system - it is a paradigm shift. Instead of past-oriented models that work with fixed threshold values and sets of rules, AI analyzes large volumes of heterogeneous data in real time. It recognizes patterns before they turn into backlogs. It predicts critical traffic situations not just when they become visible, but as soon as the conditions for them are recognizable. And it is able to capture complex correlations - for example, how a roadworks on the highway affects the traffic signal control in an urban feeder road with a time delay. [1, 2, 3, 4]

Numerous cities around the world are already demonstrating the potential of AI-supported traffic light systems. In urban traffic areas, from Singapore to Los Angeles to Amsterdam, adaptive control solutions are being used that integrate historical load data, real-time traffic counts, weather forecasts and current roadworks information. Unlike traditional, rule-based control systems, these systems work dynamically: they continuously adapt traffic light phases to the actual traffic situation instead of following fixed schedules. The results are similar in many places - noticeable. Reductions in congestion times, improved traffic flow, particularly on secondary axes, and a measurable improvement in air quality thanks to less stop-and-go traffic and more efficient traffic flows. Such examples show that the use of AI is not only technically possible, but also practically effective and scalable - especially in metropolitan areas with high traffic volumes and complex infrastructure. [5, 6]

AI-based systems also make an important contribution to traffic optimization in the higher-level freeway network. Modern deep learning models are able to accurately predict bottlenecks and traffic jams with a lead time of up to 60 minutes and thus initiate targeted countermeasures such as detour, speed reductions or infrastructure adjustments well in advance. These proactive interventions significantly reduce the risk of congestion and enable a faster and more precise response than conventional, reactive traffic control procedures. Scientific studies show that advanced AI-supported forecasting methods in the field of transportation can make traffic management significantly more efficient and adaptive. [7, 8, 9]

Of course, artificial intelligence is not a panacea. Its effectiveness depends largely on the quality and availability of the underlying data. It needs an infrastructure that provides up-to-date information in high resolution and frequency - from traffic sensors and weather data to roadworks reports. The so-called "black box problem" also poses a challenge: Many AI systems make decisions based on highly complex internal calculations, the traceability of which is limited for human users. This can weaken trust in the technology, especially in safety-critical applications. [10, 11]

Another obstacle is the often outdated infrastructure. Many traffic lights, traffic recording devices and networks date back to a time when digitalization was not yet an issue. At best, they only provide selective data - too little to feed AI systems. In addition, there is often a lack of standardized interfaces that would enable integration into existing control centers or systems at the various administrative levels - from the city to the federal states to the federal government. And yet - the benefits are obvious. AI-based traffic management systems not only improve traffic flow and reduce emissions - above all, they create a basis for decision-making for measures that could previously only be taken reactively or on the basis of estimates. They enable coordination between urban and supra-regional networks, recognize interactions and make them controllable.

For AI to develop its potential, we need the courage to invest, but also to open up. Sensor technology must be expanded, data platforms created, responsibilities clarified and pilot projects made possible. Above all, however, a rethink is needed: traffic planning and management must no longer be viewed separately from data analysis and AI development. Instead, transport authorities, IT departments and planning bodies should work together on solutions - with clear objectives, but also with room for testing.

Because one thing is clear: the transportation of tomorrow is not only electric or automated - it is above all data-driven. And it needs tools that can understand, evaluate and control complex systems in real time. Artificial intelligence is one of these tools. Now is the time to get to grips with it.

Author: Erik Schaarschmidt

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