Calibrating Car-Following Model Considering Measurement Errors

Car-following model has important applications in traffic and safety engineering. To enhance the accuracy of model in predicting behavior of individual driver, considerable studies strive to improve the model calibration technologies. However, microscopic car-following models are generally calibrated by using macroscopic traffic data ignoring measurement errors-in-variables that leads to unreliable and erroneous conclusions. This paper aims to develop a technology to calibrate the well-known Van Aerde model. Particularly, the effect of measurement errors-in-variables on the accuracy of estimate is considered. In order to complete calibration of the model using microscopic data, a new parameter estimate method named two-step approach is proposed. The result shows that the modified Van Aerde model to a certain extent is more reliable than the generic model