On-Board Monitoring techniques, defined as using commercial trains to monitor the track status, have garnered much attention recently. While data come in relatively low quality, OBM is a low-cost and efficient alternative to traditional monitoring methods. Thus, funded by the ETH Mobility Initiative project OMISM, this study checks the viability of applying such data in several prediction models.
