At InnoTrans 2024, the latest breakthroughs weren’t just about futuristic concepts—they tackled today’s challenges head-on. With innovations ranging from operational safety to passenger experience, leaders and data experts in the sector face a pivotal moment. This article offers a concise overview of the most significant data and AI developments presented at InnoTrans, with insights from over 27 organizations.
At Eraneos, we have a strong footprint in the public transport sector’s digital transformation. One of our areas of expertise is to select or develop tailored AI solutions in both business and IT domains. By showcasing the most promising developments at InnoTrans, we aim to empower industry leaders to navigate this complex, data-driven ecosystem effectively, enhancing their operations and services.
AI Innovations Solving Public Transport’s Biggest Challenges
Looking back at the last InnoTrans trade fair, it’s abundantly clear that the public transport industry is undergoing a significant digital transformation through the strategic implementation of artificial intelligence technologies. Here we observed industry leaders deploying AI solutions across three primary sectors: operational safety, infrastructure maintenance, and passenger services. Major companies such as Siemens Mobility, Alstom, and multiple specialized firms are investing substantially in these technologies to address critical industry challenges, including operational efficiency, safety improvements, and resource optimization.
Operational Domain: Autonomous and Semi-Autonomous Systems
In the operational domain, companies are focusing on developing and implementing autonomous and semi-autonomous systems. Start-up companies such as Futurail and OTIV are creating AI-powered solutions for train operations that range from driver assistance to full autonomy. We observed many small computer vision startups that hugely overlap, advanced driver assistance systems are currently deployed in many public transport vehicles via the manufacturer. The additional benefit that we see are in auxiliary detection of roadside objects and anomalies like people near the track or vegetation that will interfere with the track.
Winning the Data Analytics Race in Transport & Logistics
Learn how Data Analytics is empowering companies in the Transport & Logistics sector to remain relevant and gain competitive advantage.

Infrastructure maintenance: predictive analytics and robotics
Concurrently, infrastructure maintenance is being revolutionized through predictive analytics. AI-driven solutions for infrastructure maintenance like switch monitoring as developed by KONUX combine hardware solutions with AI. Well-known names in rail like Vossloh and Tesmec Rail have introduced cloud-based platforms that aggregate and analyze sensor data to optimize maintenance schedules and reduce downtime. During our visit at the Tesmec stand we also observed how AI is being used to augment diagnostics of railway systems to solve and prevent failures.
AI is transforming reactive maintenance into proactive prevention, with systems capable of predicting and preventing failures across the entire railway infrastructure.
For the first time we spotted robotics solutions at InnoTrans where Siemens, the German and Italian railways together with NextGen Robotics are working robotics in infrastructure maintenance for applications like inspection and installation, this is an area where we expect large advancements and benefits in the coming years.
Enhancing passenger experience using AI
The passenger experience sector is seeing equally significant advancements. Enterprise Bot is utilizing AI for automated customer interactions, while Nomad Digital has developed sophisticated passenger counting systems for optimized train loading. Companies like GMV and GIRO are implementing AI algorithms for improved scheduling and resource allocation in public transport operations.
In the coming years, AI is expected to enable fully integrated mobility ecosystems where passengers can enjoy seamless travel experiences through smart ticketing, real-time updates, and personalized route suggestions based on their preferences and historical travel patterns.
These developments are underpinned by a broader industry shift towards integrated, data-driven solutions that combine multiple technologies. Edge computing, being pioneered by companies like Teldat, enables real-time data processing directly on trains, while communication systems from T-Systems and cloud platforms facilitate centralized analysis and management. This comprehensive approach to AI implementation is enabling railway operators to enhance operational efficiency, improve safety standards, and deliver superior service quality while addressing industry-wide challenges such as driver shortages and maintenance optimization.
Current limitations and future prospects for AI in rail
While the application of AI in the railway sector shows promising developments across a wide array of applications, it is important to note that current implementations remain largely compartmentalized. Most solutions target specific aspects of public transport operations, effectively addressing the “low-hanging fruit” of industry challenges. As a result, the overall impact of AI on public transport remains somewhat limited, although individual components are becoming increasingly intelligent and sophisticated.
The convergence of AI technologies is breaking down silos in railway management, paving the way for holistic, interconnected systems that synergize every aspect of mobility.
Moving towards integrated AI solutions in Public Transport
The next two years are expected to be pivotal as the industry moves toward developing more comprehensive, end-to-end AI solutions for public transport operations. This evolution from targeted applications to integrated, holistic solutions represents the next step in railway AI implementation. At Eraneos, this transition is being actively facilitated through collaboration with industry partners, combining advanced Data & AI capabilities with expert knowledge in transport and logistics. Our approach ensures that future AI solutions not only address specific operational challenges but also create synergistic benefits across the entire public transport ecosystem.
The progression toward more integrated AI solutions will likely yield more substantial improvements in operational efficiency, passenger experience, and overall system performance. As the industry continues to mature in its AI adoption, the focus will shift from implementing isolated smart components to developing comprehensive, interconnected systems that can manage and optimize multiple aspects of public transport operations simultaneously.
So, if you are interested in having a discussion on data and AI solutions in the public transport sector, for inspiration or for specific challenges, feel free to contact us. We have a team of +150 specialized consultants in public transport bringing extensive expertise across various domains, including asset management, rail operations, passenger services, and IT transformation. We’re ready to help you navigate the complex landscape of AI in mobility and develop tailored solutions that drive efficiency, safety, and innovation in your organization.
AI: Are you ready to go beyond Proof of Concept?
Want to know all about overcoming the challenges of implementing AI and achieving cross-functional success? Our AI and Innovation newsletter has you covered.
