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Comparative analysis of traffic state estimation ─ Cumulative counts-based and trajectory-based methods

机译:交通状态估计的比较分析─基于累积计数和基于轨迹的方法

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摘要

The Macroscopic Fundamental Diagram (MFD) relates space-mean density and flow. Since the MFD represents the area-wide network traffic performance, studies on perimeter control strategies and network-wide traffic state estimation utilising the MFD concept have been reported. Most previous works have utilised data from fixed sensors, such as inductive loops, to estimate the MFD, which can cause biased estimation in urban networks due to queue spillovers at intersections. To overcome the limitation, recent literature reports the use of trajectory data obtained from probe vehicles. However, these studies have been conducted using simulated datasets; limited works have discussed the limitations of real datasets and their impact on the variable estimation. This study compares two methods for estimating traffic state variables of signalised arterial sections: a method based on cumulative vehicle counts (CUPRITE), and one based on vehicles’ trajectory from taxi Global Positioning System (GPS) log. The comparisons reveal some characteristics of taxi trajectory data available in Brisbane, Australia. The current trajectory data have limitations in quantity (i.e., the penetration rate), due to which the traffic state variables tend to be underestimated. Nevertheless, the trajectory-based method successfully captures the features of traffic states, which suggests that the trajectories from taxis can be a good estimator for the network-wide traffic states.
机译:宏观基本原理图(MFD)涉及空间平均密度和流量。由于MFD代表了整个区域的网络流量性能,因此已经报道了有关利用MFD概念进行边界控制策略和网络范围流量状态估计的研究。先前的大多数工作都是利用固定传感器(例如感应环路)的数据来估算MFD,这可能会由于交叉口处的队列溢出而导致城市网络中的偏差估算。为了克服该限制,最近的文献报道了使用从探测车获得的轨迹数据。但是,这些研究是使用模拟数据集进行的。有限的工作讨论了实际数据集的局限性及其对变量估计的影响。这项研究比较了两种估算信号化动脉段交通状态变量的方法:一种基于累计车辆计数(CUPRITE)的方法,另一种基于出租车全球定位系统(GPS)日志中的车辆轨迹的方法。这些比较揭示了澳大利亚布里斯班的滑行轨迹数据的某些特征。当前的轨迹数据在数量(即穿透率)上有局限性,因此交通状态变量往往被低估。然而,基于轨迹的方法成功地捕获了交通状态的特征,这表明出租车的轨迹可以很好地估计网络范围内的交通状态。

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