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This paper addresses the specific problem of estimating the dasiaconicitypsila and dasiacreep coefficientspsila values of a conical railway wheelset, which vary significantly as the vehicle runs along straight tracks with lateral irregularities. The performances of the continuous-time (C-T) least-squares error (LSE), least-absolute error (LAE) and least-absolute error with variable forgetting factor (LAE+VFF) estimators that employ the linear integral filter (LIF) method are compared. For each experiment, the LAE + VFF estimator performed the best because the algorithm combines the least-absolute error identification that has an instrumental variable element to overcome estimation bias problem and the variable forgetting factor for fast tracking and smooth steady-state estimation. The estimator, designed based on a fifth order wheelset model was then directly applied to a 14th-order two-axle railway vehicle. The LAE + VFF estimator produced similar estimated front and rear wheelset parameters values, and hence simplifying the estimation process of the more complex 2-axle railway vehicle model.