Abstract
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.
Highlights
Car-following models can be used to describe the process of drivers following each other and the interaction between adjacent vehicles in the same lane
It is often supposed that the independent variables are measured exactly and only the dependent variable has errors associated with measurement
The results show that for the modified Van Aerde carfollowing model, the mean estimate error for distance headway is 7.7% which is better than the generic model
Summary
Car-following models can be used to describe the process of drivers following each other and the interaction between adjacent vehicles in the same lane. The car-following models have been generally calibrated by using macroscopic traffic data and validated by comparing outputs aggregated at a macroscopic level [2, 7, 10,11,12]. Using macroscopic data to calibrate a microscopic model would ignore differences of vehicle behavior (such as speed choice and headway choice) within a traffic stream. The model dependent and independent variables are appointed subjectively. More and more studies indicated that measurement errors-in-variables (EIV) can yield a considerable bias in the estimation results and reduce reliability of the models [9, 14, 15]
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