发表机构
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对公交AVL数据处理的痛点，提出了一套数据清洗方法并开发了开源R包，经印第安纳波利斯3000次行程数据集验证，其处理高效、轨迹误差低，可用于估计交通信号性能指标。
AI 中文摘要
公交车辆产生的自动车辆定位（AVL）数据在性能研究中极具价值，但将原始AVL点转化为车辆启停的详细视图却十分繁琐：这些数据集稀疏、嘈杂且易出现错误。尽管近期研究已探索了重构车辆随时间位置轨迹的方法，但常用技术较为复杂，且不存在开源工具帮助从业者处理原始AVL数据。我们通过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包，来实现该工作流并重构车辆轨迹，使从业者能够轻松制定自定义微观性能指标。我们使用来自印第安纳州印第安纳波利斯的包含超过3000次行程的大型AVL数据集，演示了该工作流、评估了该包的效率，并通过估计各种交通信号性能指标展示了轨迹的实用性。最后，我们使用交叉验证量化了不同轮询频率下重构轨迹的误差。我们发现，所提出的数据处理方法和工具效率很高，在该大型数据集上仅需约3分钟的处理时间，且位置估计的误差较低（在15秒的轮询频率下，均方根误差低于10米）。估计的信号性能指标可为未来走廊沿线信号编程的评估提供参考。总之，本文为希望利用公交AVL数据构建车辆启停循环详细视图的从业者提供了实用指南和配套工具箱。
英文摘要
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.
Comments26 pages, 13 figures, R package available at https://cran.r-project.org/web/packages/transittraj/index.html
