公交车辆生成的自动车辆定位（AVL）数据在运营绩效研究中具有重要价值，但将原始 AVL 点转化为反映车辆停站—启行过程的详细轨迹十分繁琐：此类数据集通常稀疏、含噪，且易出现错误。尽管近期研究已探索了若干用于重构车辆时序位置轨迹的方法，但常用技术往往较为复杂，且目前尚无开源工具可协助实务人员处理原始 AVL 数据。本文填补该空白，提出一套完整的公交 AVL 数据清洗方法，并提供一个基于开放数据标准构建的开源 R 软件包，以实现该工作流并重构车辆轨迹，使实务人员能够便捷地构建定制化的微观绩效指标。我们利用来自美国印第安纳州印第安纳波利斯市、涵盖逾 3,000 个班次的大型 AVL 数据集，演示了该工作流，评估了该软件包的运行效率，并通过估计多种交通信号性能指标验证了所重构轨迹的实用性。最后，我们采用交叉验证法，在不同定位采样频率下量化了重构轨迹的位置误差。结果表明，所提出的數據處理方法與工具效率较高，在大型数据集上仅需约 3 分钟处理时间；位置估计误差较低（15 秒采样频率下的均方根误差低于 10 米）。所估计的信号性能指标可为后续沿线走廊信号配时方案的评估提供依据。综上，本文为希望利用公交 AVL 数据构建车辆停站—启行周期精细视图的实务人员提供了一份实用指南与配套工具箱。

Automatic vehicle location (AVL) data produced by transit vehicles is invaluable in performance studies, but turning raw AVL points into a detailed view of vehicle stop-and-gos is burdensome: the datasets are sparse, noisy, and prone to blunders. While recent research has explored methods of reconstructing trajectories describing the position of vehicles over time, the common techniques can be complex, and no open-source tools exist to help practitioners process the raw AVL. We fill this gap by proposing a thorough methodology for cleaning transit AVL data and providing an open-source R package, built on open data standards, to implement the workflow and reconstruct vehicle trajectories, allowing practitioners to easily formulate custom microscopic performance metrics. Using a large AVL dataset with over 3,000 trips from Indianapolis, Indiana, we demonstrate the workflow, evaluate the package's efficiency, and demonstrate the utility of the trajectories by estimating various traffic signal performance metrics. Finally, we use cross-validation to quantify the error in reconstructed trajectories at various polling frequencies. We find that the proposed data processing methodology and tool are efficient, requiring roughly 3 minutes of processing time on the large dataset, and error in position estimates is low (root mean square error under 10 meters at polling frequencies of 15 seconds). The estimated signal performance metrics can inform future evaluations of signal programming along the corridor. In sum, this paper serves as a practical guide and complementary toolbox for practitioners seeking to use transit AVL data to build a detailed view of vehicle stop-and-go cycles.