Modeling, simulation, and AI are key technologies for the holistic, data-driven development of sustainable mobility solutions.

1.1 Background of the Statement 

Sustainable mobility requires a deep understanding of cross-sector and cross-domain relationships - in both passenger and freight transport. This is where modeling, simulation, and artificial intelligence come into play: they make it possible to systematically capture interactions within and between mobility systems, simulate realistic “what-if” scenarios, and develop well-founded measures based on facts.

Thanks to data-driven optimization methods, both local and system-wide effects can be analyzed transparently. Digital twins also make it possible to observe mobility systems in real time and improve them in a targeted way - this significantly increases their efficiency, adaptability, and robustness. In this way, simulation becomes a central tool for strategic mobility planning.

1.2 Example at the Level of Individual Vehicles or Mobility Units

Digital models of individual vehicles or operational units – such as buses, trains, or logistics vehicles - enable precise analysis of energy consumption, utilization, and emissi-ons. AI-supported systems can continuously evaluate this data and adapt operational strategies in real time, for example through predictive maintenance or intelligent route planning. This not only increases efficiency but also minimizes resource use.

1.3 Example at the Level of the Connected Mobility System 

A concrete example is the optimization of a multimodal traffic system comprising pedestrians, cyclists, buses, shuttles, and trains. Simulations help make transfers more comfortable, reduce waiting times, and control traffic flows intelligently. AI-supported optimization ensures that global sustainability goals, such as the reduction of CO₂ emissions, are achieved simultaneously. This creates a data-based foundation for decision-making processes that promote holistic mobility offerings beyond individual transport.

Only with modern simulation and AI technologies can the growing complexity of mobility systems be sustainably mastered. 

2.1 Background of the Statement 

Our mobility systems are becoming increasingly complex- driven by autonomous vehicles, connected infrastruc-tures, new propulsion systems, diverse forms of mobility, and the integration of renewable energy. To develop sustainable solutions in this complex environment, new tools are needed: simulation and artificial intelligence.

These technologies help make complex interrelationships visible, enable well-informed decisions, and drive sustainable development in a targeted way. Simulations, for example, allow different scenarios to be tested - such as how a change in a vehicle or infrastructure affects energy consumption or emissions. AI can identify pat-terns, propose optimal solutions, and flag potential issues early on. This helps conserve resources, shorten develo-pment cycles, and achieve environmental goals at the same time.

2.2 Example at the Level of Individual Vehicles or Mobility Units

Modern vehicles are no longer just mechanical systems - they are highly connected, software-driven systems. With the help of simulation and AI, developers can evaluate, analyze, and understand how new propulsion technologies, materials, or driver assistance systems will behave even before building a prototype. This saves time, reduces costs, and avoids unnecessary material waste.

2.3 Example at the Level of the Connected Mobi-lity System

In cities and regions, a wide variety of mobility modes intersect -buses, trains, bicycles, cars, e-scooters, charging infrastructure, and more. Simulations make it possible to digitally model and analyze these complex systems: How can traffic be managed more efficiently? Where does ex-panding infrastructure make the most sense? How can emissions be effectively reduced?

AI helps evaluate large volumes of data and supports sustainable decision-making, while simulation provides a range of scenarios to inform those decisions - enabling environmentally friendly, future-ready mobility.

Simulation and AI enable true interdisciplinary collaboration

3.1 Background of the Statement

Future-proof mobility systems can only be designed sustainably if different disciplines - from vehicle engineering and traffic planning to software development - work closely together. This is exactly where simulation and artificial intelligence come into play: through standardized data formats and interfaces, they create a common work-ing foundation that breaks down silos and enables cross-system innovation. AI-driven simulations provide objective, data-based decision-making foundations and enable optimizations that go beyond disciplinary or organizational boundaries. This opens the door to entirely new digital applications - such as interdisciplinary mobility apps built on simulation data from vehicle technology, infrastructure, and user behavior. At the same time, this shared data foundation fosters collaboration across companies and industries - an important step toward the connected, intermodal mobility systems of the future.

3.2 Example at the Level of Individual Vehicles or Mobility Units

In the development of modern vehicles - such as electric, autonomous, or connected vehicles - different disciplines work together, including mechanics, software, energy management, and more. AI-supported simulations make it possible for these areas to operate on unified models and shared data foundations. This leads to more efficient de-velopment processes, reduces interface-related issues, and opens up new optimization potential - for example in the interaction between powertrain systems, sensor technology, and energy consumption.

3.3 Example at the Level of the Connected Mobility System

On a higher level, standardized simulation data enables collaboration between different stakeholders - such as traffic planners, mobility service providers, municipalities, and app developers. Through a shared data foundation, urban traffic simulations, vehicle data, and user behavior can be linked to develop new mobility services or improve existing ones. This interdisciplinarity is essential for implementing efficient, connected, and sustainable mobility systems.

Sustainable mobility requires an actively developed shared understanding among all stakeholders - enabled by simulation and AI as key tools.

4.1 Background of the Statement

Modern mobility systems are characterized by complex, cross-sector and cross-domain interactions - for example between technology, environmental conditions, infrastruc-ture, regulation, and user behavior. Today, these interdependencies are often viewed from isolated perspectives, leading to differing interpretations, conflicting objectives, and acceptance challenges.

Simulation and AI create a shared and transparent know-ledge base: they make complex interactions understand-able, comparable, and verifiable - regardless of the stake-holders’ professional backgrounds. Using established methods such as statistical design of experiments, sensiti-vity analyses, and data-driven models, assumptions can be systematically varied, causal relationships revealed, and the impacts of measures assessed without the need for risky or costly real-world testing.

This creates a reliable common basis for decision-making, enabling better alignment between technological innovation, political objectives, and societal expectations - and helping sustainable mobility solutions not only to be developed, but also broadly understood and accepted.

4.2 Example at the Level of Individual Vehicles or Mobility Units

Simulations can analyze critical driving situations - such as sudden braking, evasive maneuvers, or accident scenarios. AI evaluates these scenarios so that all stakeholders - engineers, traffic planners, and authorities - gain a shared understanding of risks, safety margins, and necessary measures. In this way, the understanding of safety requirements and optimized systems becomes collectively shared.

4.3 Example at the Level of the Connected Mobility System

Insights gained from individual vehicle simulations can also be transferred into large-scale traffic simulations, for example for analyzing urban metropolitan areas. Once it is understood how weather conditions or other influencing factors affect vehicle performance, potential disrupti-ons or accident risks within the overall traffic system can be predicted more accurately. Here too, simulation and AI create a common understanding among all stakeholders - from traffic planners and policymakers to the general public - thereby enabling the planning of resilient, climate-adaptive, and efficient transportation systems.

Simulation and AI create facts - and thereby foster an objective dialogue about sustainable mobility. 

5.1 Background of the Statement

The discussion about the mobility of the future is often emotional and shaped by uncertainty, conflicting interests, or even misinformation. This is where simulation and artificial intelligence come into play: they provide transparent, comprehensible, and data-driven insights into complex interrelationships. Whether regarding CO₂ emissions, traffic density, or infrastructure planning, realistic models help make the consequences of decisions visible and understandable.

In addition, the use of VR and AR technologies can make complex scenarios directly experienceable - both for decision-makers and for the broader public. This creates a new quality of societal dialogue: moving away from assumptions and toward fact-based decision-making. Simulations support the fair comparison of alternatives and help stakeholders collaboratively develop viable and sustainable solutions.

5.2 Example at the Level of Individual Vehicles or Mobility Units

Even within individual mobility systems - such as a car, bus, or train - AI and simulation enable well-founded and transparent decision-making. For example, new powertrain technologies, driver assistance systems, or operational strategies can be simulated in advance and evaluated with regard to energy consumption, emissions, and safety.

These realistic models make technical interdependencies transparent - both for developers and for external stake-holders. By presenting simulation results in an understandable way, it becomes possible to demonstrate how a specific technology contributes to emission reduction or how driving behavior affects energy efficiency. This creates objective foundations for discussions surrounding vehicle development, regulation, and sustainable innovation.

5.3 Example at the Level of the Connected Mobility System

Simulations make it possible to visualize the effects of new mobility strategies - such as car-free city centers or new public transport routes - at an early stage. Visualizations using VR or AR help policymakers, public authorities, and citizens better understand and actively shape the consequences of such measures. This fosters trust, acceptance, and a solid foundation for sustainable decision-making. 

Simulation and AI are key technologies for making well-founded, transparent, and sustainable decisions in mobility.

6.1 Background of the Statement

Whether in the development of new transportation concepts or the optimization of existing infrastructure, sound decisions today require reliable data, clear analyses, and an understanding of systemic interdependencies. Simula-tion and artificial intelligence provide exactly that: they make it possible to realistically model complex scenarios, transparently reveal causal relationships, and objectively evaluate different courses of action.

Data-driven technologies replace assumptions with fact-based insights. This builds trust, reduces risks, and improves the transparency and traceability of decisions - both in industry and in politics and society. Simulations not only improve the quality of individual decisions, but also signi-ficantly accelerate the overall decision-making process.

Thanks to increasing computing power and technological advances, large volumes of data can now be analyzed in a short time - even for highly complex questions. In a rapidly changing world, AI-supported simulation is often the only way to act quickly, reliably, and sustainably.

6.2 Example at the Level of Individual Vehicles or Mobility Units

When designing new vehicles - for example with regard to low-emission powertrains or automated driving - simulation enables the objective evaluation of a wide range of technical solutions. AI supports this process by intelligent-ly analyzing large amounts of test data and enabling well-founded decisions regarding design, safety, and energy efficiency. As a result, development processes become not only faster and more cost-efficient, but also more sustainable.

6.3 Example at the Level of the Connected Mobility System

In transportation and urban planning, simulation-based analyses help stakeholders understand the impact of political or infrastructural measures - such as new public transport services or the redistribution of road space - before implementation. The combination of AI and simulation creates transparent decision-making foundations for public authorities, planners, and political actors, enabling the implementation of resource-efficient and widely accepted mobility solutions.

Simulation and AI increase cost efficiency and strengthen Europe’s competitiveness in sustainable mobility - particularly through accelerated development processes.

7.1 Background of the Statement

Simulation is a highly efficient means of developing sustainable mobility solutions quickly, demand-oriented, and cost-effectively. Different scenarios can be tested virtually, allowing optimization potentials to be identified at an early stage. In combination with artificial intelligence, simulations become more precise, enabling data-based decisions on a robust foundation.

A key advantage is time savings: new technologies can be rapidly modeled, evaluated, and further developed. This accelerates innovation processes, reduces time-to-market, and enables a fast response to new challenges.

These efficiency gains also contribute to strengthening Europe’s technological sovereignty and enable more tar-geted resource utilization. Material usage, energy con-sumption, and production processes can be better aligned and planned more transparently, thereby reducing dependence on volatile raw material markets.

For successful implementation, stable framework conditions are essential, particularly reliable key technologies, secure supply chains, and access to necessary raw materials.

7.2 Example at the Level of Individual Vehicles or Mobility Units

By virtually testing new vehicle components - such as electric drivetrains or lightweight structures - manufacturing processes can be optimized, materials saved, and production capacities better planned. This makes development not only more cost-efficient but also more environmentally sustainable.

7.3 Example at the Level of the Connected Mobility System

In transport planning, simulations help better align mobility services with actual demand - for example in the design of charging infrastructure, the integration of new transport modes, or the management of multimodal traffic flows. AI provides the necessary scalability and precision, thereby strengthening the data-driven competitiveness of European mobility solutions.

The integration of simulation and artificial intelligence is a key prerequisite for the efficient and resource-conscious development of sustainable mobility solutions.

8.1 Background of the Statement

An efficient development and planning process for sustainable mobility requires the use of data-driven technologies. Simulation and AI enable well-founded decisions at an early stage at component, system, and overall system level - thereby shortening development times, saving resources, and avoiding misdevelopments.

The fundamental relationships and mechanisms of complex mobility systems are already known and structured in the form of principle models. However, to use these insights continuously and in a practical way, modern si-mulation technologies and AI are needed. They make these models scalable, dynamic, and applicable in everyday contexts - enabling broader practical use in real mobility environments.

To efficiently develop robust, adaptable, and sustainable solutions, a targeted use of simulation and AI is required. Appropriate model scales and methods must be selected to ensure that the benefit justifies the computational effort, allowing scenarios to be evaluated efficiently - whether for individual vehicles or complex traffic systems.

8.2 Example at the Level of Individual Vehicles or Mobility Units

In the development of an electric vehicle, simulation of the powertrain and battery management enables early optimization of energy efficiency, safety, and range. AI models identify weaknesses, analyze complex dependencies, and propose targeted improvements - even before physical prototypes are built. This saves development time, materials, and energy.

8.3 Example at the Level of the Connected Mobility System

In urban transport systems, AI-supported simulations assist in analyzing and optimizing multimodal mobility - for example through intelligent traffic flow management, demand-driven sharing services, or the integration of sustainable charging infrastructure. This enables targeted reductions in CO₂ emissions, energy consumption, and traffic congestion, and supports a more holistic approach to sustainable mobility.

VR makes it possible to present complex results from simulation and AI in a clear and intuitive way that is understandable for everyone.

9.1 Background of the Statement

Modern simulation and AI methods operate at a perfor-mance level that is hardly verifiable in detail even for experts. It is precisely this enormous computational power that makes their use so attractive - but it also carries the risk that people perceive their “computational colleagues” as a black box and feel excluded from decision-making processes. To continue making safe decisions based on calculated recommendations, results must be clear and tangible. Virtual Reality (VR) tools offer unique opportuni-ties for this: they enable complex simulation results to be understood intuitively, allow direct comparison of variants, and make interactions comprehensible through deep immersion. This builds trust in the technology and supports decision-makers at all levels in making well-founded assessments.

9.2 Example at the Level of Individual Vehicles or Mobility Units

With VR-based representations of traffic scenarios, “digital twins” of real vehicles and situations can be created. High-quality visualizations make simulation results tangible: users can act as virtual road users, test different scenarios, switch perspectives, observe processes in slow motion, or make hidden physical processes visible. This makes complex mechanisms, data flows, and technical details under-standable and enables well-founded analysis.

9.3 Example at the Level of the Connected Mobility System

VR technologies also enable mobility systems to be viewed from a higher-level perspective: digital twins of entire traffic systems allow traffic flows, bottlenecks, or disruptions to be analyzed in an overview visualization. This “bird’s-eye view” helps decision-makers recognize interdependencies and effectively simulate measures. By running simulations faster than real time, future scenarios can also be explored in advance in a comprehensible way. This enables traffic control centers to make more informed and effective decisions.