Despite numerous theoretical models, only limited field experiments have been conducted to investigate traffic oscillation propagation, and the relationships between traffic oscillation features (e.g., period, speed variation, spacing and headway) have not received quantitative analysis. This study conducts a set of field experiments designed to inspect such relationships. In these experiments, 12 vehicles equipped with high-resolution global positioning system (GPS) devices following one another on public roads, and the lead vehicle was asked to move with designed trajectory profiles incorporating various parameters. Measurements of five features are extracted from processing the field vehicle trajectory data with a time-domain method. Frequency analysis is also proposed with the Fourier transform method to verify the effectiveness of the features measured by the time-domain method. Compared to the frequency-domain method, the time-domain method yields more measurements with comparable quality and is more robust on trajectories with a small number of oscillation cycles. Then, a series of linear regression analyses reveal a number of new findings on the relationships between these features. For example, the time gap between two consecutive vehicles is negatively correlated with the speed standard deviation of the preceding vehicle and the initial speed of the following vehicle. It is also positively correlated with the average speed of the preceding vehicle and the initial spacing. The findings are helpful in constructing new microscopic traffic models better describing traffic oscillation dynamics. To illustrate this benefit, revised car following models are proposed to capture the relationship between time gap and other features. The simulation results show that the revised models yield better prediction accuracy (in range of 18% to 40% with the oscillation experiment dataset and in range of 30–63% with the stationary experiment dataset) than the classical models on reproducing real-world trajectories.

尽管有许多理论模型，但仅进行了有限的现场实验来研究交通振动的传播，并且交通振动特征（例如，周期，速度变化，间距和车距）之间的关系尚未得到定量分析。这项研究进行了一组旨在检验这种关系的现场实验。在这些实验中，有12辆配备有高分辨率全球定位系统（GPS）装置的车辆在公共道路上相继行驶，并且要求领先车辆以包含各种参数的设计轨迹轮廓进行行驶。通过使用时域方法处理野战车辆的轨迹数据来提取五个特征的测量值。还提出使用傅里叶变换方法进行频率分析，以验证通过时域方法测量的特征的有效性。与频域方法相比，时域方法可产生更多具有可比质量的测量结果，并且在具有少量振荡周期的轨迹上更为鲁棒。然后，一系列线性回归分析揭示了有关这些特征之间关系的许多新发现。例如，两个连续的车辆之间的时间间隙与在前车辆的速度标准偏差和在后车辆的初始速度负相关。它也与在前车辆的平均速度和初始间距呈正相关。这些发现有助于构建新的微观交通模型，更好地描述交通振荡动力学。为了说明这种好处，提出了修订后的汽车跟随模型以捕获时间间隙与其他特征之间的关系。仿真结果表明，与经典模型相比，修正后的模型在再现真实轨迹方面具有更好的预测精度（在振动实验数据集中范围为18％至40％，在固定实验数据集中范围为30–63％） 。