Department of Civil, Environmental and Geomatic Engineering, ETH, 8093, Zurich, Switzerland.
Sci Rep. 2023 Jan 20;13(1):1121. doi: 10.1038/s41598-023-28202-1.
As more and more trajectory data become available, their analysis creates unprecedented opportunities for traffic flow investigations. However, observed physical quantities like speed or acceleration are often measured having unrealistic values. Furthermore, observation devices have different hardware and software specifications leading to heterogeneity in noise levels and limiting the efficiency of trajectory reconstruction methods. Typical strategies prune, smooth, or locally modify vehicle trajectories to infer physically plausible quantities. The filtering strength is usually heuristic. Once the physical quantities reach plausible values, additional improvement is impossible without ground truth data. This paper proposes an adaptive physics-informed trajectory reconstruction framework that iteratively detects the optimal filtering magnitude, minimizing local acceleration variance under stable conditions and ensuring compatibility with feasible vehicle acceleration dynamics and common driver behavior characteristics. Assessment is performed using both synthetic and real-world data. Results show a significant reduction in the speed error and invariability of the framework to different data acquisition devices. The last contribution enables the objective comparison between drivers with different sensing equipment.
随着越来越多的轨迹数据可用，它们的分析为交通流研究创造了前所未有的机会。然而，观测到的物理量（如速度或加速度）往往具有不切实际的值。此外，观测设备具有不同的硬件和软件规格，导致噪声水平的异质性，并限制了轨迹重建方法的效率。典型的策略包括修剪、平滑或局部修改车辆轨迹，以推断出合理的物理量。过滤强度通常是启发式的。一旦物理量达到合理的值，在没有地面真实数据的情况下，就不可能进一步改进。本文提出了一种自适应物理信息轨迹重建框架，该框架可以迭代地检测最佳过滤幅度，在稳定条件下最小化局部加速度方差，并确保与可行的车辆加速度动态和常见的驾驶员行为特征兼容。评估是使用合成和真实世界的数据进行的。结果表明，该框架在不同的数据采集设备上显著降低了速度误差和不变性。最后一个贡献使具有不同传感设备的驾驶员之间能够进行客观比较。
