We propose a new methodology for calibrating Wiedemann-99 vehicle-following parameters for mixed traffic (different conventional vehicle classes) based on trajectory data. The existing acceleration equations of the Wiedemann model are modified to represent more realistic driving behavior. Exploratory analysis of simulation data revealed that different Wiedemann-99 model parameters could lead to similar macroscopic behavior, highlighting the importance of calibration at the microscopic level. Therefore, the proposed methodology is based on optimizing performance measures at the microscopic level (acceleration, speed, and trajectory profiles) to estimate suitable calibration parameters. Further, the goodness of fit for the observed data is sensitive to the numerical integration method used to compute vehicles’ velocity and position. We found that the calibrated parameters using the proposed methodology perform better than other approaches for calibrating mixed traffic. The results reveal that the calibrated parameter values and, consequently, the thresholds that delineate closing, following, emergency braking, and opening regimes, vary between two-wheelers and cars. The window (in the relative speed versus gap plot) for the unconscious following is larger for cars while the free-flow regime is more extensive for two-wheelers. Moreover, under the same relative speed and gap stimulus, two-wheelers and cars may be in different regimes and display different acceleration responses. Thus, accurate calibration of each vehicle’s parameters is essential for developing micro-simulation models for mixed traffic. The calibration analysis results of strict and overlapping staggered car following signify an impact of staggered car following compared with strict car following which demands separate calibration for strict and staggered following.

我们提出了一种基于轨迹数据校准混合交通（不同传统车辆类别）的 Wiedemann-99 车辆跟随参数的新方法。对 Wiedemann 模型的现有加速度方程进行了修改，以表示更真实的驾驶行为。对仿真数据的探索性分析表明，不同的 Wiedemann-99 模型参数可能导致相似的宏观行为，突出了微观层面校准的重要性。因此，所提出的方法是基于优化微观层面（加速度、速度和轨迹曲线）的性能指标来估计合适的校准参数。此外，观测数据的拟合优度对用于计算车辆速度和位置的数值积分方法很敏感。我们发现使用所提出的方法校准的参数比其他校准混合交通的方法表现更好。结果表明，经过校准的参数值以及因此描述关闭、跟随、紧急制动和打开方式的阈值在两轮车和汽车之间有所不同。无意识跟随的窗口（在相对速度与间隙图中）对于汽车来说更大，而对于两轮车来说，自由流动机制更广泛。此外，在相同的相对速度和间隙刺激下，两轮车和汽车可能处于不同的状态并表现出不同的加速响应。因此，精确校准每辆车的参数对于开发混合交通的微观仿真模型至关重要。