A computer-implemented method for maintenance planning for a train wheel, the method comprising: receiving, in a processor, measurement data of the train wheel, wherein receiving the measurement data comprises receiving, in the processor, vertical load data of the train wheel as the train wheel rolls across a measuring station arranged at a rail, the vertical load data comprising a plurality of vertical load time-series measurements (P1, P2, P3, P4), each measurement being made by a different one of a plurality of sensor units (U1, U2, U3), which comprise vertical load sensors (L1, L2), arranged at the load measuring station; determining, in the processor, using the measurement data, whether the train wheel will need to undergo maintenance in the future; calculating, in the processor, the dynamic coefficient, which dynamic coefficient is a ratio of a maximum dynamic load to a static load according to the following relation: dynamic coefficient = max. QForce static QForce, wherein the max. ( QForce) is a maximum value of the vertical load of the train wheel determined across the plurality of vertical-load time-series measurements (P1, P2, P3, P4), and the static ( QForce) is an average value of the vertical load of the train wheel determined across the plurality of vertical load time-series measurements (P1, P2, P3, P4); and predicting, in the processor, if the train wheel will need to undergo maintenance in the future, a maintenance point, wherein determining whether the train wheel will need to undergo maintenance comprises: generating, in the processor, using the dynamic coefficient and a forecasting model, forecasted dynamic coefficients, and predicting, in the processor, using the forecasted dynamic coefficients, the maintenance point by determining a critical time and/or a critical distance at which the forecasted dynamic coefficients exceed a critical dynamic coefficient threshold. receiving, in a processor, measurement data of the train wheel, wherein receiving the measurement data comprises receiving, in the processor, vertical load data of the train wheel as the train wheel rolls across a measuring station arranged at a rail, the vertical load data comprising a plurality of vertical load time-series measurements (P1, P2, P3, P4), each measurement being made by a different one of a plurality of sensor units (U1, U2, U3), which comprise vertical load sensors (L1, L2), arranged at the load measuring station; determining, in the processor, using the measurement data, whether the train wheel will need to undergo maintenance in the future; calculating, in the processor, the dynamic coefficient, which dynamic coefficient is a ratio of a maximum dynamic load to a static load according to the following relation: dynamic coefficient = max. QForce static QForce, wherein the max. ( QForce) is a maximum value of the vertical load of the train wheel determined across the plurality of vertical-load time-series measurements (P1, P2, P3, P4), and the static ( QForce) is an average value of the vertical load of the train wheel determined across the plurality of vertical load time-series measurements (P1, P2, P3, P4); and predicting, in the processor, if the train wheel will need to undergo maintenance in the future, a maintenance point, wherein determining whether the train wheel will need to undergo maintenance comprises: generating, in the processor, using the dynamic coefficient and a forecasting model, forecasted dynamic coefficients, and predicting, in the processor, using the forecasted dynamic coefficients, the maintenance point by determining a critical time and/or a critical distance at which the forecasted dynamic coefficients exceed a critical dynamic coefficient threshold. generating, in the processor, using the dynamic coefficient and a forecasting model, forecasted dynamic coefficients, and predicting, in the processor, using the forecasted dynamic coefficients, the maintenance point by determining a critical time and/or a critical distance at which the forecasted dynamic coefficients exceed a critical dynamic coefficient threshold.