混合浮动车数据和基于点的传感器数据来推断不同交通流条件下的行车速度

IF 0.7 Q4 TRANSPORTATION European Transport-Trasporti Europei Pub Date : 2023-02-01 DOI:10.48295/et.2023.91.5
Giulia Del Serrone
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引用次数: 1

摘要

如今,智能交通可以依靠创新工具来了解道路系统状况,比如从所谓的浮动汽车数据(FCD)中提取的运行速度数据。探测车辆在交通流中向操作中心发送大量的行驶信息,这些信息通过GPS探测系统收集,特别是地理位置、日期和时间、方向和速度等信息。由于从这些车辆中提取的样本只占整个车队的一小部分,因此本文提出了与基于点的交通传感器获得的数据进行分析和比较。因此,本研究分析了电感环路检测器和微波雷达传感器收集的数据,这些数据提供了时域内整个交通流的信息,特别是旨在确定自由流动速度的时间带。然后,将这些基于点的控制单元的数据结果与探测车辆的数据结果进行融合,比较有约束和无约束交通流两种情况下的运行速度。
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Blending of Floating Car Data and Point-Based Sensor Data to Deduce Operating Speeds under Different Traffic Flow Conditions
Nowadays, smart mobility can rely on innovative tools for the knowledge of road system conditions, like operating speed data extracted from the so-called Floating Car Data (FCD). Probe vehicles in the traffic flow send to operation centres a large amount of travel information, collected through GPS detection systems, especially with regard to geolocation, date and time, direction and speed. As the sample deriving from these vehicles represents a tiny portion of the entire vehicular fleet, in this paper an analysis and a comparison with data obtained by point-based traffic sensors is proposed. Therefore, the study analyses data collected by inductive loop detectors and microwave radar sensors, that provide information on the entire traffic flow in the time domain, in particular with the aim to identify free flow speed time bands. Afterwards, by means of the fusion between the results obtained from the data coming from these point-based control units and the ones coming from the probe vehicles, a comparison of the operating speeds in the two conditions of constrained and unconstrained traffic flow is performed.
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来源期刊
CiteScore
2.30
自引率
0.00%
发文量
19
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