Tracking vehicle trajectories and fuel rates in phantom traffic jams: Methodology and data
Tracking vehicle trajectories and fuel rates in phantom traffic jams: Methodology and data
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DOI：
10.1016/j.trc.2018.12.012
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
Fangyu Wu;Raphael E. Stern;Shumo Cui;Maria Laura Delle Monache;R. Bhadani;Matt Bunting;M. Churchill
中科院分区：
文献类型：
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作者：
Fangyu Wu;Raphael E. Stern;Shumo Cui;Maria Laura Delle Monache;R. Bhadani;Matt Bunting;M. Churchill
Sugiyama等人（2007）进行的交通实验是交通研究领域的一项开创性工作。在实验中，一组车辆被指示在均匀间距的圆形轨道上行驶。隔离的实验环境为自由流交通和虚幻交通波的研究提供了一个安全、经济、可控的环境。本文介绍了一种在这种环境中实现数据收集过程自动化的新方法。具体来说，车辆轨迹是通过360度摄像头测量的，燃油率是通过车载诊断（OBD-II）扫描仪记录的。来自360度摄像头的视频数据随后由离线无监督计算机视觉算法处理。为了验证数据收集方法，该技术随后在一系列8个实验中进行评估。分析表明，采集的数据精度较高，平均位置偏差小于0.002 m，标准差较小，为0.11 m。位置数据还产生可靠的速度估计：导出的速度偏差仅为0.02 m/s，标准偏差为0.09 m/s。生成的轨迹和燃油率数据可以很容易地用于研究人类驾驶行为，校准微观仿真模型，开发油耗模型，以及调查发动机排放。为了促进未来的研究，源代码和数据在网上公开。
The traffic experiment conducted by Sugiyama et al. (2007) has been a seminal work in transportation research. In the experiment, a group of vehicles are instructed to drive on a circular track starting with uniform spacing. The isolated experimental environment provides a safe, economic, and controlled environment to study free flow traffic and phantom traffic waves. This article introduces a novel method that automates the data collection process in such an environment. Specifically, the vehicle trajectories are measured using a 360-degree camera, and the fuel rates are recorded via on-board diagnostics (OBD-II) scanners. The video data from the 360-degree camera is then processed by an offline unsupervised computer vision algorithm. To validate the data collection method, the technique is then evaluated on a series of eight experiments. Analysis shows that the collected data are highly accurate, with a mean positional bias of less than 0.002 m and a small standard deviation of 0.11 m. The positional data also yields reliable velocity estimates: the derived velocities are biased by only 0.02 m/s with a small standard deviation of 0.09 m/s. The produced trajectory and fuel rate data can be readily used to study human driving behaviors, to calibrate microsimulation models, to develop fuel consumption models, and to investigate engine emissions. To facilitate future research, the source code and the data are made publicly available online.