Calibration and Validation of Probabilistic Discretionary Lane-Change Models


Lane changes (LCs) are necessary in traffic flow operations. They cause variations in flow over lanes and in some cases confirm the start of congestion. Whereas calibration and validation are commonly used with automobile-following models, this can be not common apply with LC models. Even then, it is not clear what calibration and validation entails for probabilistic LC models. Thus, this paper reviews methodologies to calibrate and validate probabilistic LC models, both microscopically and macroscopically. A chance is usually used in calibration however will not intuitively show the quality of the model. An example shows that it's possible to own the model calibrated and validated with correct parameters all having the identical error in the validation as within the calibration, but the quality of the model remains unhealthy. Employing a chance ensures the stochastic effects are well captured, but the conclusion is that for validation purposes, one will higher use a live that has physical interpretation and gives a worth indicating the standard of the model for the aim for which it wants for use.

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