Predictions of railway traffic are needed for the design of robust timetables and real-time traffic management. These tasks can be effectively performed only by using train running time models that reliably describe actual speed profiles. To this purpose calibration of model parameters against field data is a necessity. In this paper a simulation-based optimization approach is proposed to calibrate the parameters of the train dynamics equations from field data collected. Furthermore, a procedure for the estimation of train lengths has been developed. This method has been applied to trains with different rolling stock running on the Rotterdam-Delft corridor in the Netherlands. Probability distributions for each parameter are derived which can be used for simulation studies. The results show that the train length estimation model obtained good computation accuracy and the calibration method was effective in estimating the real train path trajectories. It has been observed that some of the parameters of tractive effort and resistance do not affect the train behaviour significantly. Also, the braking rate is significantly smoother than the default value used by the railway undertaking while calibrated resistance parameters tend to have lower mean than defaults. Finally, the computational efficiency of the approach is suitable for real-time applications.