Increasing rail transit ridership by improving passenger ride comfort.

In urban rail transit systems, passenger ride comfort (PRC) significantly influences ridership levels. Various factors affect PRC, with track geometry playing a crucial role. Among geometry parameters, the minimum required tangent length between consecutive railway curves is particularly significant. While this parameter affects passenger comfort considerably, it has received less attention in current design practices. This study addresses this gap by optimizing tangent length requirements to improve passenger comfort. To this end, a vehicle-track interaction model was established and subsequently validated through comprehensive field measurements. The influence of curve radius, tangent length, and speed on the PRC was investigated through a parametric analysis, considering various curve configurations. As a result, a large data bank was created, and a model was developed using data mining techniques to predict minimum tangent lengths. The accuracy and computational performance of the model were discussed. The results obtained indicate that reverse curves require 5% longer tangent lengths and have a 9% higher ride comfort index compared to those of compound curves. The model optimizes tangent lengths for speeds above 80 km/h, leading to improved ride comfort. This improvement contributes to increased rail transit ridership.