Many models attempt to predict the behavior of a system. These models may be physical, mathematical,statistical, or simulation representations of the system. Within the transportation field, physical models can be scale models of the geographical area of interest, mathematical models can be queuing models, statistical models can be platoon dispersion models, and simulation models can be meso- or microsimulation models. These models may operate on cross-section data, which represent a snapshot of a system at a particular point in time, or on time series data, which represent the “movement” of a system through time.
If the appropriate model for the system is known, a dataset is used to calibrate the parameters in the model and then the model is applied. If the model is not known, then procedures are necessary to select from a range of models. As part of this selection process, a commonly used procedure is to split the dataset into two portions for training and testing purposes. The training portion, which is usually the larger, is used to calibrate the parameters in the model, and then the testing portion is used to assess the accuracy of the calibrated model in reproducing observed behavior. If the performance of the model with the testing dataset is deemed adequate, then the two datasets are pooled and the model recalibrated. Sometimes there is either insufficient data of acceptable quality to enable this partition to take place or no obvious way of dividing the datasets. In such cases, a with-replacement sampling approach may be adopted to construct the two datasets. To accurately assess without bias a model’s goodness-of-fit, the modeler must first determine the values of the calibration parameters and then assess the performance of that model
This paper, while incorporating forecasting models, is not concerned with a detailed study of the relative merits of these models, but with methods of assessing their ability to produce useable forecasts. In particular, this paper does not concern itself with the accepted iterative procedures of model identification, model estimation, and model diagnosis. It is assumed that these stages have been successfully completed and that the practitioner is now interested in how the model performs.
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