AI Trends 2025: How artificial intelligence will transform the mobility sector

Artificial intelligence (AI) has evolved from being a buzzword into a cornerstone of technological progress across industries. In 2025, it will solidify its role as a transformative force in the mobility sector. From enhancing traffic modeling to revolutionizing real-time traffic management and enabling integrated transportation systems, there is a huge potential for AI in transportation.  In his latest LinkedIn article Christian U.  Haas, CEO of PTV Group, part of Umovity, shares AI trends for mobility in 2025.

AI in Transport Modeling

Transport modeling plays a vital role in helping cities and organizations make informed decisions about mobility and infrastructure. By simulating and validating plans, it ensures preparedness for future challenges. AI powered tools are taking this process to the next level, enabling faster, more precise, and scalable solutions.

Christian U. Haas explains: “AI is already optimizing the creation and calibration of transport models, making processes faster and more accurate. A prime example is PTV Model2Go, which uses smart automation technology and machine learning to deliver a basic transport model of any city or metropolitan area in the world in just one week.”

Only recently, the PTV software development team integrated an AI tool to estimating employee data into Model2Go. This AI-based method is faster than traditional ways of collecting and analyzing data. It is also more scalable and accurate.

“We are continuously expanding this process, for example by including open data to refine training models”, notes Haas.

AI in Traffic Management

Modern traffic management systems manage large amounts of data. They capture everything from vehicle trajectories to near-miss incidents and red-light violations. By using AI, these systems can improve efficiency. They provide real-time insights, predict possible problems, and optimize traffic flow.

One notable innovation is the use of image processing to extract critical traffic data from visual media. “AI can identify vehicles at intersections, track pedestrian movement, and detect traffic violations or near-misses to trigger safety countermeasures automatically,” shares Christian U. Haas.

According to PTV’s CEO natural language processing (NLP) is also reshaping the field. By facilitating more intuitive interactions with traffic management tools, NLP enhances decision-making during critical scenarios. For instance, AI systems can provide actionable recommendations to operators during traffic disruptions, speeding up responses and improving accuracy.

Haas adds: “Finally, by combining ML methods with traditional model-based approaches, we can significantly improve the accuracy and reliability of traffic and mobility short-term predictions. This hybrid approach leverages the strengths of both methodologies, creating smarter and proactive solutions.”

AI as the Base for a Holistic Approach

The true power of AI is in data analytics. The ability to connect diverse data streams—from real-time updates to historical simulations— helps to create an integrated transportation management framework. This comprehensive approach enables organizations to make smarter, more adaptive decisions.

Umovity’s Dynamic Multimodal Network Management concept, launched in 2024, embodies this vision. “Our Dynamic Multimodal Management concept, integrates management, operational planning, and decision support into a unified system, enabling cities and organizations to shape and coordinate mobility across all modes of transportation,” explains Haas. “It enables cities and organizations to shape and coordinate mobility across all modes of transportation.”

The aim is to create a connected system for dynamic mobility planning and control. This will use from diverse traditional and non-traditional sources. Examples are advanced sensors like Econolite’s EVO Radar, infrastructure-less live data such as Floating Car Data, and long-term data repositories.

Innovative AI technologies and model-based methodologies will also be included. This ecosystem enables cities to respond swiftly and effectively to both planned and unexpected changes in mobility demand.

By using innovative data sources like Econolite’s EVO Radar and Floating Car Data, alongside AI-driven analytics, cities can adjust to planned events and unexpected changes in travel patterns. This creates a connected ecosystem that enhances responsiveness and resilience.

PTV’s CEO adds: “With advanced tools for planning, disruption management, and congestion mitigation, cities will be equipped to tackle challenges in real-time and over the long term. By integrating operational data into transport planning and applying advanced analytics like AI, we’re shaping a future where urban mobility is smarter, more responsive, and truly connected.”

AI trend 2025: Catalyst for Smarter Mobility

In 2025, AI developments will drive the future of transportation, fostering sustainability, inclusivity, and adaptability. Achieving this vision requires the industry to embrace continuous innovation and collaboration.Partnerships will be very important in reaching AI’s full potential. They will help test new datasets and develop new ideas.

Christian U. Haas concludes: “We must maintain a lean and agile approach, staying attuned to market evolution and remaining committed to innovation and open to working with new technologies, solutions, and applications. Partnerships and collaborations will be vital in validating innovative ideas and testing new datasets. At Umovity, we are committed to advancing these technologies, unlocking new opportunities, and shaping the future of mobility.”