Cantisani Giuseppe, Del Serrone Giulia, Mauro Raffaele, Peluso Paolo, Pompigna Andrea
Department of Civil, Constructional and Environmental Engineering, University of Rome La Sapienza, Via Eudossiana 18, 00184 Rome, Italy.
Department of Civil, Environmental and Mechanical Engineering, University of Trento, Via Mesiano 77, 38123 Trento, Italy.
Sensors (Basel). 2023 Aug 3;23(15):6922. doi: 10.3390/s23156922.
This paper focuses on the analysis of traffic streams on two-lane highways, which are crucial components of transportation networks. Traffic flow measurement technologies, such as detection stations, radar guns, or video cameras, have been used over the years to detect the level of traffic and the operating conditions. This type of sensor can record a large amount of data which is useful to evaluate and monitor road traffic conditions, and it is possible to identify free-moving and constrained vehicles by processing the collected data. This study introduces an exponential headway model to identify the headway threshold above which vehicles can be considered as unconditioned. However, this value could identify vehicles which still retain some autonomy in their speed and maneuvering. Therefore, an additional criterion to distinguish between apparently and actually conditioned vehicles has been introduced, analyzing the speed differences between a vehicle and the preceding one. Three-month sequences of traffic monitored through radar devices placed on some Italian two-lane roads have been analyzed and an exponential headway model has been introduced, as an illustrative example. The results show that introducing the criterion of maneuvering freedom can significantly improve traffic flow analysis, modifying the starting critical values of 4 and 8 s per each studied section, to 2.5 and 3 s, approaching the values suggested by international manuals for traffic flow quality analysis.
本文重点分析双车道公路上的交通流，双车道公路是交通网络的关键组成部分。多年来，交通流量测量技术，如检测站、雷达测速仪或摄像机，一直被用于检测交通水平和运行状况。这类传感器可以记录大量数据，这些数据对于评估和监测道路交通状况很有用，并且通过处理收集到的数据有可能识别自由行驶和受约束的车辆。本研究引入了一个指数车头时距模型来确定车头时距阈值，超过该阈值车辆可被视为不受限制。然而，该值可能会识别出在速度和操纵方面仍保留一定自主性的车辆。因此，引入了一个额外的标准来区分表面上和实际上受限制的车辆，即分析一辆车与前车之间的速度差异。作为一个示例，分析了通过放置在意大利一些双车道道路上的雷达设备监测到的为期三个月的交通序列，并引入了一个指数车头时距模型。结果表明，引入操纵自由度标准可以显著改善交通流分析，将每个研究路段的起始临界值从4秒和8秒修改为2.5秒和3秒，接近国际交通流质量分析手册建议的值。
