Mind the Gap: A Comparative Analysis of AI Deployment in the Swiss Federal Railways and Other Railway Companies
The world of railways is becoming ever more dynamic, and one of the driving forces behind much of its innovation is artificial intelligence (AI). Our thesis explores the challenges of the railway industry and the transformative power of AI on the Swiss Federal Railways (SBB).
Timon Reding & Nicolas Stucky, 2023
Bachelor Thesis, Schweizerische Bundesbahnen SBB
Betreuende Dozierende: Martin Sterchi
Keywords: Artificial Intelligence, Railway Industry
SBB and the European railway industry face complex challenges such as energy crises, personnel shortages, urbanism, and cybersecurity. Simultaneously, AI is reshaping many industries, including railways. This thesis evaluates the current AI landscape of SBB in comparison to other railway operators (Deutsche Bahn AG, SNCF, JR East, Greater Anglia, Trenitalia, and Amtrak), identifies gaps and provides recommendations for SBB to optimise its AI adoption.
The research conducts a comparative analysis of AI deployment in SBB relative to other railway companies worldwide with a focus on Central Europe. Information is drawn from academic literature, industry reports, internal interviews, and SBB's internal documents. A dual analytical approach is taken. First, a PESTEL analysis evaluates the various challenges faced by the European railway sector, examining political, economic, social, technological, environmental, and legal aspects. Then, a SWOT analysis identifies specific AI use cases' strengths, weaknesses, opportunities, and threats.
Challenges identified for the European railway sector, particularly the Swiss Federal Railways, included numerous domains from political (e.g., Ukraine war) to legal (e.g., changing legislation). In AI adoption, the railway sector broadly focuses on areas like maintenance and inspection, traffic management, safety, autonomous driving, and passenger mobility. Foreign railway operators like Deutsche Bahn AG, SNCF, and others deploy AI for predictive maintenance, autonomous operations, and enhanced passenger services. On the other hand, SBB's AI landscape comprises similar endeavours, as well as their own unique projects in each area. However, gaps were noted, particularly in IoT-based predictive maintenance, autonomous driving, AI-based customer support, smart CCTV applications and AI-based timetabling. Recommendations for SBB included implementing an AI strategy, developing an ethical framework for the responsible and robust use of the technology, forming a cross-organisational AI team, enhancing collaboration with other transportation companies, and further exploring AI-empowered solutions.
Studiengang: Business Administration International Management (Bachelor)
Fachbereich der Arbeit: