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AI in transportation

AI in Transportation: How Artificial Intelligence Transforms Mobility

Artificial Intelligence (AI) is reshaping how cities plan, operate, and optimize mobility systems. Across public transit, traffic management, and urban planning, AI is no longer a future vision but an active driver of change. This transformation comes at a critical moment: cities worldwide face rising congestion, aging infrastructure, and growing travel demand.

By integrating AI into transportation planning and operations, authorities can improve traffic flow, enhance public transport scheduling, and support data-driven strategies for more sustainable and resilient mobility. The following sections highlight key application areas and real-world examples of how AI in transportation is already delivering measurable benefits in public transport, strategic planning, and traffic management.

Smarter Public Transport with AI-Powered Predictive Analytics

Public transportation agencies worldwide have an increasing number of AI-based tools at their disposal, enabling them to optimize daily operations – such as reducing waiting times, better matching supply with demand, and enhancing the passenger experience. Predictive analytics help operators forecast ridership patterns, optimize vehicle deployment, and respond more effectively to service disruptions. AI-based systems surpass classical data analysis through the powerful ability to identify patterns in large quantity of historical data and turn them into more reliable predictions on future occurrences on similar matters. 

Benefits include: 

  • Better forecast of demand on basis of pattern analysis
  • Shorter response times to disruptions or sudden changes in demand
  • Better crew scheduling, avoiding inefficiencies and overtime
  • Improved vehicle deployment to reduce empty runs and energy waste 

But AI’s potential goes far beyond operational gains. Today, more and more public authorities are beginning to embrace AI as a foundation for long-term, data-driven mobility planning. By analyzing both real-time and historical data, transport agencies can forecast future demand, evaluate infrastructure investments, and simulate the effects of different policy scenarios. This approach is steadily growing more powerful with a broader availability of relevant data. 

San Antonio: Applying AI models to improve public transit planning 
In San Antonio, Texas, researchers have tested the use of Large Language Models (LLMs) to support public transport planning. These models analyze publicly available data, such as GTFS (General Transit Feed Specification), to optimize routes, anticipate passenger demand, and personalize travel recommendations. The study found that LLMs can help reduce waiting times, improve passenger satisfaction, and enhance resource allocation across the network. 

This example shows a clear trend: Public transport operators are aiming for more attractive, more customer-oriented offers while striving for operational excellence with improved efficiency. With tools like PTV Lines, agencies can easily transform data into real-world impact - designing smarter, more adaptive transit networks that respond to evolving passenger needs. 

Enhance your public transport efficiency and strategic planning with PTV Lines. 

Strategic Transport Planning with AI-Powered Modeling

AI is not only transforming how we operate transport systems - it’s also redefining how we plan them. Strategic transport planning depends on understanding why and how people and goods move through a city, now and in the future. With AI-powered modeling tools, planners can simulate different infrastructure or policy scenarios, evaluate their impact, and make data-based investment decisions. 

By combining data from traffic sensors, public transport systems, and socio-demographic as well as structural data, AI helps create detailed, real-world representations of urban mobility. These models support long-term prediction and decision-making and enable cities to align planning with sustainability goals, capacity needs, and travel behavior. 

With tools like PTV Visum and the advanced data-driven automation from PTV Model2Go, cities can drastically accelerate their transport planning processes - from weeks to hours - while maintaining accuracy and relevance of their results. PTV Model2Go can provide powerful machine-learning-based predictions of the precise spatial distribution of workplaces within a city, that serves as valuable input data to predict work-related mobility events, which form a large share of traffic in peak hours. 

Berlin Example: PTV has developed example models using Model2Go for various cities, including Berlin. These models allow urban planners to assess how new developments or policy changes will affect traffic volumes, travel behavior, and network performance. 

Contact our experts to learn how PTV Model2Go accelerates your city’s mobility planning. 

AI-Powered Intelligent Traffic Management & Congestion Reduction

AI-based traffic management is revolutionizing how cities address congestion. Traditional systems operate on static rules or pre-set time intervals. In contrast, AI-driven systems analyze real-time traffic data under knowledge of historic traffic patterns and prediction of future traffic states to make dynamic decisions that respond instantly to current conditions. 

From data to action | By integrating data from sensors, connected vehicles, traffic cameras, weather systems and transport models, AI traffic management systems can: 

  • Predict congestion before it occurs
  • Adapt traffic light timings in real time
  • Prioritize public transport and emergency vehicles
  • Minimize idle times and emissions
  • Prioritize vulnerable road users like elderly people, children or handicapped pedestrians to increase traffic safety
  • Act to proactively prevent critical traffic even before they occur

These systems use machine learning algorithms that continuously learn from observed outcomes and improve, resulting in increasingly accurate and efficient traffic control over time.

York: Using AI-powered traffic modeling to manage congestion in real time 

The city of York in the UK has implemented PTV Optima to create a city-wide real-time transport model - one of the first of its kind in the country. The system integrates live data from traffic sensors, GPS probes, and signal controllers to continuously monitor road conditions. Using AI-powered algorithms and predictive modeling, York can forecast traffic states up to an hour in advance and adjust signal strategies dynamically. 

This enables traffic managers to respond proactively to congestion, incidents, and changing demand patterns - reducing delays, improving journey times, and cutting emissions. The city also uses the system to evaluate traffic management scenarios before deploying them, ensuring that decisions are grounded in reliable data and simulation. 

This practical example demonstrates how AI in transportation is already optimizing urban mobility at scale - moving from reactive control to predictive, real-time traffic management. 

PTV’s role in intelligent traffic management 

PTV Group’s tools like PTV Optima and PTV Flows are designed to bring these AI capabilities to cities of all sizes. They enable: 

  • Short-term traffic forecasts (5–60 minutes ahead)
  • Immediate scenario testing and simulation
  • Network-wide optimization in real time
  • Proactive alerting based on current and forecasted traffic conditions 

These tools integrate seamlessly with existing infrastructure and provide an immediate return on investment through improved flow and reduced congestion-related costs. 

Discover PTV Optima and PTV Flows for smarter traffic management. 

Enhancing Urban Access with AI-Powered Mobility Analysis

Artificial Intelligence is reshaping how cities evaluate and improve access to mobility. By leveraging machine learning and advanced modeling techniques, planners can move beyond static accessibility maps and instead generate dynamic, real-time insights into how different populations interact with the transport network. 

AI helps identify underserved areas, detect access barriers across socio-demographic groups, and simulate the impact of new transit lines, mobility hubs, or policy changes, before they’re implemented. 

Use case: Data-driven equity analysis for better mobility outcomes.

Transport agencies can apply AI to: 

  • Cluster areas with low public transport access using socio-economic data
  • Predict how accessibility will evolve with population shifts or infrastructure investments
  • Prioritize projects that increase equitable access to jobs, education, and healthcare 

To complement these insights, planners can use tools like PTV Access to visualize and compare accessibility across cities and neighborhoods. While not AI-based itself, PTV Access helps communicate data-driven findings clearly and supports evidence-based decision-making in planning and policy. 

Modeling (e.g. with PTV Visum) and accessibility tools (like PTV Access) together provide a powerful basis for designing inclusive, responsive transport systems. 

See how AI-enhanced modeling and data-driven accessibility tools help cities close mobility gaps and promote inclusive urban transport. 

Enhancing Shared Mobility with AI-Driven Forecasting

Artificial Intelligence (AI) is transforming shared mobility by enabling more accurate demand forecasting, optimizing vehicle distribution, and reducing unnecessary detours. By analyzing real-time and historical data, AI algorithms can predict passenger demand, allowing shared mobility providers to position vehicles more effectively, reduce waiting times, and improve service efficiency. 

One example of this is the #transmove project, carried out in Hamburg between 2020 and 2024. Funded by the German Federal Ministry for Digital and Transport (BMDV) and the Hamburg Authority for Transport and Mobility Transition, the project developed AI-based short- and long-term mobility forecasts to support sustainable urban mobility strategies. Its goal: to better anticipate demand patterns and traffic dynamics using agent-based models and machine learning. 

#transmove combined simulation frameworks like PTV Visum to model individual mobility behavior across the city. The results helped planners and traffic coordinators test infrastructure changes in advance and deliver dynamic information to end users via apps like HVV Switch - empowering both public authorities and citizens to make smarter decisions in real time. 

Although the project has concluded, its insights remain highly relevant as cities continue to scale shared mobility services and align them with emission reduction goals. 

Discover how AI-powered forecasting can elevate your shared mobility services and learn more about the #transmove initiative by PTV: #transmove Project Overview: PTV Group – #transmove: AI-based Mobility Forecasts 

AI as a Driver of Sustainable Mobility 

Artificial Intelligence is a powerful enabler of greener transport systems, helping cities and operators reduce emissions and energy consumption through smarter planning and real-time optimization. By applying AI to vehicle routing, fleet deployment, and traffic signal control, measurable environmental gains can be achieved. 

Use case: Dynamic traffic optimization in Project COMO in Essen 

In Essen in Germany, an implementation of PTV Flows and PTV Optima helps – in combination with a detailed emission calculation in microscopic simulation – to reduce emissions of carbon dioxides, nitrogen oxides and other traffic-related pollutants with an emission-sensitive traffic management. The combination of dynamic transport models and machine learning allows for a forecast of traffic development and proactive management actions with adapted traffic signal programs.  

System-wide benefits of AI-powered transport modeling 

Tools like PTV Visum and PTV Optima allow transport planners and traffic managers to foresee the environmental impact of operational changes or policy measures, such as low-emission zones, optimized transit frequencies, or green-wave signal timing. These scenario evaluations enable data-driven decisions that balance efficiency with sustainability goals. 

While the exact emission reduction depends on local conditions and can be investigated in greater detail with microscopic simulation in PTV Vissim, studies and pilot projects consistently show that AI-based traffic and transit optimization can lead to tangible reductions in fuel consumption, travel time, and emissions. 

Discover how AI-powered transport modeling and real-time optimization tools from PTV help cities turn sustainability goals into measurable results. 

Accelerating Autonomous Mobility with AI-Driven Simulation

One of the most complex applications of AI in transportation is the development of autonomous vehicles (AVs), which rely on AI for perception, decision-making, and behavior prediction. Major technology providers like NVIDIA, supplying AI platforms and simulation frameworks for autonomous vehicle development, are promoting end-to-end learning (from sensor input to control output) as an approach they call AV 2.0. However, training and validating these AI systems in real-world traffic is costly, time-consuming, and potentially unsafe. Studies have shown that purely real-world validation to ensure the functional safety of an autonomous vehicle is simply not feasible due to the billions of test miles required. This is where high-fidelity simulation environments become indispensable, moving testing into the virtual world and dramatically accelerating development processes through shift-left testing (earlier development-phase testing) and massive parallelization.

AI meets simulation 

AI-powered autonomous systems must be exposed to thousands of critical and rare traffic events (edge cases) to learn safe and human-like behavior. One effective approach is to define a comprehensive envelope of critical scenarios around the less challenging driving situations within an Operational Design Domain (ODD) to ensure functional safety under all conditions.

Tools like PTV Vissim Automotive enable developers to: 

  • Create complex, multi-modal traffic scenarios with realistic agent behavior
  • Integrate a system under test (model of a component, operating strategy, full vehicle) into the responsive test environment for realistic interactions
  • Simulate all interactions between AVs, human drivers, cyclists, or pedestrians
  • Use smart presets to easily modify the intensity of the test environment e.g. with aggressive driving behavior, stochastically distributed driver errors like lack of attention or misestimation of speed and the influence of adverse weather conditions
  • Identify and reproduce critical edge cases such as sudden lane changes, crashes, harsh decelerations, or erratic behavior from other vehicles 

These simulations provide training data for AI models (e.g. reinforcement learning) or a validation environment for large virtual test drives and allow continuous validation of AV control systems under controlled, repeatable conditions. And the best, due to the agent-based intelligent behavior of all traffic participants, there is no need to script individual critical scenarios. It is sufficient to set the traffic scene through the road network, its control logic like traffic signals and let the vehicle inputs fill the network. The rest will happen automatically through the dynamic interaction of all traffic participants with each other and the network. 

Used by leading automotive innovators 

PTV Vissim Automotive is used by OEMs, Tier 1s and other AV developers worldwide to accelerate development cycles, improve the robustness of AI-driven control systems, and ensure safety and compliance across diverse road environments. 

Get started with PTV Vissim Automotive to simulate real-world complexity for AI-driven autonomous mobility.

Enhancing Transport Safety with AI-Based Incident Detection

Safety is a critical pillar of urban mobility and AI is increasingly being used to detect, predict, and respond to risks faster and more effectively than traditional systems. With real-time video analytics, computer vision, and machine learning, cities can move from reactive traffic control to proactive safety management. 

AI-powered video analytics for early incident detection 

Modern traffic monitoring systems use AI to analyze video feeds and identify anomalies such as: 

  • Collisions or stalled vehicles
  • Near misses at intersections
  • Harsh decelerations
  • Pedestrians entering roadways unexpectedly
  • Dangerous driving behavior (e.g. abrupt lane changes or speeding) 

These systems can automatically alert traffic control centers or emergency services, reducing response times and improving situational awareness. 

Real-world applications in urban safety 

In several European cities, AI-based incident detection has been implemented at accident-prone intersections, enabling: 

  • Faster emergency response by up to 30%
  • Data collection on near misses to improve infrastructure planning
  • Adaptive safety measures such as dynamic speed limits or warning signals 

AI also plays a growing role in pedestrian and cyclist safety, helping cities design infrastructure that accounts for vulnerable road users more effectively. 

Discover how AI-powered traffic management systems enhance road safety through real-time incident detection and proactive response strategies. 

Conclusion 

The transformative power of modelling, simulation and AI in transportation is evident - addressing critical challenges from public transport efficiency to sustainable urban mobility. Cities that embrace those digital tools can offer residents more accessible, efficient, and eco-friendly transportation systems, ensuring they are future-ready. 

Ready to take the next step with AI in transportation? Explore PTV Group’s intelligent mobility solutions and discover how our AI-powered tools can help your city move smarter, safer, and more sustainably. 

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