Smartphones have become widely accessible and advanced, pocket-sized computers capable of sensing, sending, and processing an ever-increasing variety of data. Modern phones are, for example, equipped with sensors such as accelerometers and GPS-sensors. Meanwhile, an increase in rail traffic leads to an increase in the need of having a reliable and comfortable railway system, an objective which can be achieved through additional monitoring. Through mobile crowdsensing, the smartphone data from passengers could potentially provide an additional and almost continuous information source on the railway system health state.

This thesis explores to what extent smartphone accelerometer measurements on board of in-service passenger trains can be used to monitor railway track quality. The thesis contains a literature review and a survey with European infrastructure managers. They identify that repeatability, as a result of operational variance, is a key research gap and challenge in the implementation of this technology. The thesis assesses accelerometer characteristics from a set of contemporary smartphones through laboratory tests. The tests uncover limitations and heterogeneity in the sensing capabilities such as sampling frequencies, low-pass filters and eigenfrequencies of the devices. The thesis also makes use of a case study, in which different smartphones are placed in various positions on different in-service passenger trains, running over the same tracks in the Netherlands. The vertical acceleration signals are analysed and compared. The vehicle speed and the position of the smartphone within the car body are found to significantly influence both the frequency content and vertical acceleration amplitudes measured. Finally, the different signals are related back to a Track Quality Index based on the standard deviation of the Longitudinal Level D1 track parameter. A degree of consistency in identifying locations with a low relative track quality is displayed, albeit with a significant degree of variance due to the previously mentioned factors.