基于改进IDM-GARCH模型的速度波动不确定性研究（双语出版）

为定量分析跟驰行为中由驾驶人感知不确定性产生的速度波动不确定性，基于改进IDM-GARCH模型研究了后车速度波动存在的异方差性。首先，提出在经典智能驾驶模型中加入速度差刺激项和非对称系数，以增强速度波动方程残差项的实际意义。在此基础上，为度量速度波动的不确定性引入异方差的思想，并验证速度波动方程残差项的异方差性，最后运用广义自回归条件异方差模型对其异方差性建模。实证分析中，采用了美国联邦公路管理局主导下的下一代仿真项目中真实有效的跟驰数据。研究结果表明：改进的IDM模型能有效地拟合实际跟驰行为中后车的速度变化，且较经典IDM模型在精度上有了很大提高；同时，GARCH类模型估计的条件方差也能准确反映后车速度波动的趋势和幅度，以及不同驾驶人驾驶行为的差异。

Uncertainty of Velocity Fluctuation Based on an Improved IDM-GARCH Model（in English）

To quantitatively analyze the uncertainty of velocity fluctuation caused by driver perception uncertainty in car following behavior, the heteroscedasticity of velocity fluctuation based on the improved IDM-GARCH model was studied. First, for enhancing the practical significance of the residuals of velocity fluctuation equation, the velocity difference stimulus and the asymmetry coefficient was added to the classical IDM model. On this basis, the idea of heteroscedasticity was introduced to measure the uncertainty in velocity fluctuation, and the heteroscedasticity of the residuals of the velocity fluctuation equation was proved, and the heteroscedasticity was modeled using the GARCH model. In the empirical analysis, the real and effective car following data from the next-generation simulation project (NGSIM) led by the Federal Highway Administration was adopted. The results demonstrate that the improved IDM model can effectively fit the velocity fluctuation of the following car, and the accuracy of the model is improved greatly compared with the classical IDM model. Simultaneously, the conditional variance estimated by the GARCH model can accurately reflect the trend and range of velocity fluctuation of the following car and the difference in the driving behavior among different drivers.