This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. MOEs are key parameters in determining the quality of service at signalized intersections. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from realtime videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, fuel consumption, CO2 emissions, and fundamental diagrams. Results of the various MOEs were found to be promising, which confirms that the use of traffic data collected by drones has many applications. We also demonstrate that estimating MOEs in real-time is achievable using drone data. Such models have the ability to track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions. These microscopic models have the advantage of capturing the impact of transient vehicle behavior on various MOEs.
翻译:本文介绍了利用飞行无人驾驶飞机所收集的车辆轨迹数据在信号十字路口计算各种有效性计量的新方法。MOE是确定信号十字路口服务质量的关键参数。具体地说,这项研究调查了在希腊雅典一个繁忙的三路信号十字路口使用无人机原始数据的情况,并以PNEUMA实验的开放数据倡议为基础。我们利用从实时视频中提取的数据的微微镜法和冲击波分析,估计了最大排队长度,是否、何时和在何时发生溢漏、车辆停留、车辆旅行时间和延迟、坠毁率、燃料消耗、二氧化碳排放和基本图表。各种MOE的结果很有希望,证实了使用无人驾驶飞机所收集的交通数据有许多用途。我们还表明实时估计MOE是使用无人机数据可以实现的。这些模型能够跟踪从高度饱和度到高度饱和度的车辆状况,从而使得模型能够考虑任何交通流量条件,从高度饱和度低到高度饱和高度饱和高度的车辆的状态。这些微光谱模型能够捕捉到各种机动模型的优势。