Development of a Scalable Digital Twin for Tram and Light-rail Infrastructure based on Open Data for Early Prediction of Rail and Track Defects

Abstract

Due to an increasing passenger demand in rail-based transportation and a desire for sustainable mobility, rail infrastructure is nowadays confronted with increased loads requiring timely and efficient maintenance regimes (Holzfeind et al., 2025). To improve maintenance scheduling and give rail infrastructure operators better insights into the state of their networks, a comprehensive digital twin based on open data has been developed. The digital twin allows to connect sensor data from vehicles to railway assets and enables the development of custom algorithms for condition-based maintenance of railway tracks. For practical tests and validation of the digital twin, smartphones were placed in various trams and lightrail vehicles in the city of Frankfurt (Main) to record vibration and geolocation data over a period of more than a year. The results demonstrate that infrastructure quality changes can be automatically detected and monitored through the developed digital twin framework using a low-cost measurement set-up. Hereby, new capabilities for proactive maintenance scheduling and resource allocation emerge, and infrastructure operators can prioritize interventions effectively and ensure safe and comfortable railway operations.