Statistical procedures are usually used in modeling transportation needs by assuming that the data used does not have any errors, but this condition is unlikely to occur in practice. This study aims to examine the types of errors in model calibration and forecasting. The method used in this research is descriptive, containing a study of the types of errors that usually arise from the sampling process to the calibration and forecasting processes. A good combination of modeling complexity with data accuracy will produce data output that is close to reality. The types of errors that occur during sampling, calibration, and forecasting include specification, grouping, measurement, sampling, calculation, and transfer errors. By knowing the type of error, the model can produce a more accurate forecast output.