QuieterRail · A step change in prediction, mapping, acceptance testing and cost-effective mitigation for railway noise and vibration

QuieterRail aims to introduce a step change in predicting and mapping railway noise and vibration, in the acceptance testing of rolling stock, and in promoting cost-effective noise mitigation. The established TWINS-based approach for rolling noise is extended to include curves. Squeal noise models are developed including flanging noise and multiple wheel-rail contact points. Proposals are made for improving the CNOSSOS curve noise factor based on project results. To add flexibility to acceptance testing, transposition methods are developed and tested for situations where there are larger differences, e.g. between slab and ballast track. Measurement uncertainty in pass-by noise is studied, including the effects of site geometry. A virtual test method for the noise from freight wagons is proposed and information is gathered on the noise from trains with new propulsion technology.QuieterRail produces guidelines and a data analysis tool for cost-effective noise mitigation by controlling rail roughness using on-board measurement systems. It also provides an open-source tool for whole track system optimisation, addressing life cycle costs, noise, and vibration simultaneously. The goal is to enhance the cost-effectiveness and social benefits of railway infrastructure by reducing externalities.A fast-running hybrid vibration prediction method will be enhanced to include features pertinent to urban situations. It will be implemented in a GIS-based environment to allow mapping of vibration alongside noise. The database with soil impedance and transfer functions and building correction factors will be substantially extended. A track-independent vehicle indicator is developed to allow a first step towards quantifying the vibration emission of trains and the introduction of vibration limit values.Extensive noise and vibration measurements at a mainline site and on an urban light rail network provide for validation of the results and expansion of relevant databases.