In this paper we use traffic data from a driving simulator study to calibrate four different car-following models. We also present two applications for which the calibration results can be used. The first application relied on the advantage that driving simulator data also contain information on driver characteristics, for example, age, gender, or the self-assessment of driver behavior. By calibrating the models for each driver individually, the resulting model parameters could be used to analyze the influence of driver characteristics on driver behavior. The analysis revealed that certain characteristics, for example, self-identification as an aggressive driver, were reflected in the model parameters. The second application was based on the capability to simulate dangerous situations that require extreme driving behavior, which is often not included in datasets from real traffic and cannot be provoked in field studies. The model validity in these situations was analyzed by comparing the prediction errors of normal and extreme driving behavior. The results showed that all four car-following models underestimated the deceleration in an emergency braking scenario in which the drivers were momentarily shocked. The driving simulator study was validated by comparing the calibration results with those obtained from real trajectory data. We concluded that driving simulator data were suitable for the two proposed applications, although the validity of driving simulator studies must always be regarded.

在本文中，我们使用来自驾驶模拟器研究的交通数据来校准四种不同的跟驰模型。我们还介绍了可以使用校准结果的两种应用。第一个应用程序依赖于这样的优势，即驾驶模拟器数据还包含有关驾驶员特征的信息，例如年龄、性别或驾驶员行为的自我评估。通过为每个驾驶员单独校准模型，所得模型参数可用于分析驾驶员特征对驾驶员行为的影响。分析表明，模型参数中反映了某些特征，例如将自我识别为侵略性驱动程序。第二个应用程序基于模拟需要极端驾驶行为的危险情况的能力，这通常不包含在来自真实交通的数据集中，也不能在实地研究中引发。通过比较正常和极端驾驶行为的预测误差，分析模型在这些情况下的有效性。结果表明，所有四个跟驰模型都低估了紧急制动情况下的减速度，在这种情况下，驾驶员会瞬间受到惊吓。通过将校准结果与从真实轨迹数据中获得的结果进行比较来验证驾驶模拟器研究。我们得出的结论是，驾驶模拟器数据适用于两个提议的应用程序，尽管必须始终考虑驾驶模拟器研究的有效性。通过比较正常和极端驾驶行为的预测误差，分析模型在这些情况下的有效性。结果表明，所有四个跟驰模型都低估了紧急制动情况下的减速度，在这种情况下，驾驶员会瞬间受到惊吓。通过将校准结果与从真实轨迹数据中获得的结果进行比较来验证驾驶模拟器研究。我们得出的结论是，驾驶模拟器数据适用于两个提议的应用程序，尽管必须始终考虑驾驶模拟器研究的有效性。通过比较正常和极端驾驶行为的预测误差，分析模型在这些情况下的有效性。结果表明，所有四个跟驰模型都低估了紧急制动情况下的减速度，在这种情况下，驾驶员会瞬间受到惊吓。通过将校准结果与从真实轨迹数据中获得的结果进行比较来验证驾驶模拟器研究。我们得出的结论是，驾驶模拟器数据适用于两个提议的应用程序，尽管必须始终考虑驾驶模拟器研究的有效性。通过将校准结果与从真实轨迹数据中获得的结果进行比较来验证驾驶模拟器研究。我们得出的结论是，驾驶模拟器数据适用于两个提议的应用程序，尽管必须始终考虑驾驶模拟器研究的有效性。通过将校准结果与从真实轨迹数据中获得的结果进行比较来验证驾驶模拟器研究。我们得出的结论是，驾驶模拟器数据适用于两个提议的应用程序，尽管必须始终考虑驾驶模拟器研究的有效性。