Development of Railway Ride Comfort Prediction Model: Incorporating Track Geometry and Rolling Stock Conditions

Abstract

Passenger ride comfort (PRC) is one of the most important performance indicators of railway transportation. The current methods for computation and evaluation of railway ride comfort require measurement of train accelerations and dynamic vehicle–track interaction parameters, which is costly and sometimes impractical. Despite the importance of passenger ride comfort in design and operation of railways, there is a lack a reliable PRC prediction model in the available literature. Addressing this limitation, an effective and practical PRC prediction model/index is established in this study, taking into consideration track and rolling stock influencing parameters for the first time. For this purpose, correlations were developed between PRC levels and track geometry parameters as well as rolling stock dynamic properties. The PRC level was computed based on accelerations obtained from accelerometers installed on the wagon floor. The track geometry parameters were obtained from a track recording car. Practicability and reliability of the prediction model were discussed by applying the model in a railway line. A good agreement was shown between the PRC levels obtained from the prediction model and those of field measurements. Application of the prediction model in the real world of railway engineering is illustrated.