Estimation of Penetration Rates of Floating Car Data at Signalized Intersections
Estimation of Penetration Rates of Floating Car Data at Signalized Intersections
复制标题
信号交叉口浮动车数据渗透率估算
DOI：
10.1016/j.trpro.2021.01.026
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
H. Friedrich
中科院分区：
文献类型：
--
作者：
Fourati;W. Dabbas;H. Friedrich
浮动车数据(FCD)是一种广泛使用的数据源，特别是在提供速度和行程时间的实时和平均测量方面非常成熟。它们具有在空间和时间上扩展覆盖的优势，完全独立于任何现场设备部署。与此同时，它们存在车辆覆盖不完整的弱点，更重要的是渗透率未知。这一弱点是FCD全面使用的主要限制因素，例如确定交通流量。获得FCD数据集的渗透率并捕获其变化的直接方法是将FCD计数与固定设备的计数进行比较。该方法具有空间限制，因为只有在具有连续全计数检测器的特定位置才能知道渗透率。本文提出了一种新的方法来估计信号交叉口的平均拥堵速度，该方法基于探测器聚集拥堵密度与假设的平均拥堵密度之间的比较。轨迹首先在信号周期的基础上聚合。然后从聚集的曲线图测量探测器的宏观条件。采用基本图模型拟合法对拥堵密度进行了实证测算。在未知渗透率的商业数据集上验证了该方法的可行性。然后利用从微观模拟中随机提取的轨迹，在不同的条件和场景下检验了该方法的相关性和准确性。结果表明，当采样率达到15s时，可以估计高于5%的渗透率，误差小于10%。
Floating Car Data (FCD) are a largely used data source, particularly well established in delivering real time and average measurements of speed and travel time. They have the advantage of a spread coverage in space and time, being totally independent from any field device deployment. In the same time, they present the weakness of incomplete vehicle coverages, and more importantly unknown penetration rates. This weakness is the main limiting factor toward full scale uses of FCD such as determining traffic volumes. The direct way to obtain the penetration rate of a FCD dataset and capture its variations is to compare FCD counts with counts from a stationary device. The method has a spatial limitation since the penetration rate can be known only in particular locations where a continuous full-count detector is available. This paper suggests a novel methodology to estimate an average penetration rate at signalized intersections based on the comparison between probe-aggregated congestion density and an average congestion density supposed known. Trajectories are first aggregated on a signal cycle basis. Probe macroscopic conditions are then measured from the aggregated plot. A fundamental diagram model fitting is employed to empirically measure the congestion density. The feasibility of the method is tested on a commercial dataset of unknown penetration rate. The relevance and accuracy of the method are then examined under different conditions and scenarios with randomly extracted trajectories from a microscopic simulation. The results suggest that the penetration rate when higher than 5% can be estimated with an error below 10% for a sampling rate up to 15 seconds.
DOI：
10.1109/itsc.2019.8917023
发表时间：
2019-10
期刊：
2019 IEEE Intelligent Transportation Systems Conference (ITSC)
影响因子：
--
作者：
Fourati Walid;Aleksandar Trifunović;Morten Flesser;B. Friedrich
通讯作者：
Fourati Walid;Aleksandar Trifunović;Morten Flesser;B. Friedrich