Trajectory data serving as an essential data-source, has been widely applied in traffic flow analysis, traffic prediction and transportation management. In real situations, trajectory data is often corrupted with noises, which may introduce estimation bias and control inefficiency to intelligent transportation systems. This paper presents a novel trajectory reconstruction method which is generic for both highway and urban arterial trajectories. The reconstruction method establishes an Empirical Mode Decomposition (EMD) based Butterworth low-pass filter framework to filter the noises and simultaneously maintain physical integrity. The two-stage framework firstly applies the EMD to decompose the original trajectories into components, multiple intrinsic mode functions (IMFs), to find out the main components of different temporal-frequency characteristics. Based on that, an optimal Butterworth-filter is applied on the lower order IMFs to filter the acceleration of an unexpected high-frequency range. To test the effectiveness of our proposed method, multiple resource data-sets are applied. As results indicated that our proposed reconstruction method performs well in terms of physical trajectories integrity, high-frequency noise removal, and measurement error rejection with minimum signal distortion. Further, our method efficiently produces speed and acceleration with higher quality compared with the state-of-the-art methods.

轨迹数据作为必不可少的数据源，已广泛应用于交通流分析、交通预测和交通管理等领域。在实际情况下，轨迹数据经常被噪声破坏，这可能会给智能交通系统带来估计偏差和控制效率低下。本文提出了一种新颖的轨迹重建方法，该方法适用于高速公路和城市动脉轨迹。重建方法建立了一个基于经验模式分解 (EMD) 的巴特沃斯低通滤波器框架来过滤噪声并同时保持物理完整性。两阶段框架首先应用 EMD 将原始轨迹分解为分量、多个内在模式函数 (IMF)、找出不同时频特性的主要成分。在此基础上，对低阶 IMF 应用最优巴特沃斯滤波器以过滤意外高频范围的加速度。为了测试我们提出的方法的有效性，应用了多个资源数据集。结果表明，我们提出的重建方法在物理轨迹完整性、高频噪声去除和测量误差抑制方面表现良好，信号失真最小。此外，与最先进的方法相比，我们的方法以更高的质量有效地产生速度和加速度。应用了多个资源数据集。结果表明，我们提出的重建方法在物理轨迹完整性、高频噪声去除和测量误差抑制方面表现良好，信号失真最小。此外，与最先进的方法相比，我们的方法以更高的质量有效地产生速度和加速度。应用了多个资源数据集。结果表明，我们提出的重建方法在物理轨迹完整性、高频噪声去除和测量误差抑制方面表现良好，信号失真最小。此外，与最先进的方法相比，我们的方法以更高的质量有效地产生速度和加速度。