Improving the rail transport security requires development and implementation of neoteric monitoring and control facilities in conditions of increasing speed and intensity of the train movement and high level of terro...Improving the rail transport security requires development and implementation of neoteric monitoring and control facilities in conditions of increasing speed and intensity of the train movement and high level of terrorist threat. Use of Earth remote sensing (ERS), permitting to obtain information from large areas with a sufficiently high resolution, can provide significant assistance in solving the mentioned problems. This paper discusses the possibility of using various means of remote sensing such as satellites and unmanned aerial vehicles (UAV), also known as drones, for receiving information in different ranges of the electromagnetic spectrum. The paper states that joint using of both these means gives new possibilities in improving railroad security.展开更多
为了实时识别快速路交织区拥堵瓶颈的形成及其诱发因素,基于无人机航拍视频构建车辆轨迹数据,提出一种融合交通流不稳定性分析的交织区拥堵识别方法。识别方法由车辆轨迹提取、扰动感知模型和拥堵风险指数构建3个阶段构成。首先,通过YOL...为了实时识别快速路交织区拥堵瓶颈的形成及其诱发因素,基于无人机航拍视频构建车辆轨迹数据,提出一种融合交通流不稳定性分析的交织区拥堵识别方法。识别方法由车辆轨迹提取、扰动感知模型和拥堵风险指数构建3个阶段构成。首先,通过YOLOv4(You Only Look Once,Version 4)网络训练航拍小目标权重检测俯拍车辆,关联外观与运动特征以跟踪车辆轨迹,从而提取无人机航拍视频中的精细车辆轨迹。然后,通过提取车辆微观速度、变道、冲突信息建立车速扰动和变道交织扰动感知模型。最后,采用熵值法结合扰动信息与平均车速构建归一化的拥堵风险指数,根据交织流的拥堵风险指数识别拥堵。本文采集广州大桥数据进行案例分析与测试验证。研究结果表明:学习了小目标特征的网络在航拍场景测试的误检率和少检率均低于5%,所提取的车辆轨迹连续稳定;在交织区拥堵识别评价中,本文方法的F1值达到97.85%,明显优于基本参数识别方法,在各路段中具有较高的识别准确度和算法鲁棒性;相比平均速度指标,所提出的拥堵风险指数能够更精细灵敏地反映短时和局部的拥堵,并能够从平均车速、个体车速差异和变道交织3个维度中识别多种因素引起的交织区交通瓶颈。研究结果可为城市重点路段交通诱导与优化提供技术基础。展开更多
文摘Improving the rail transport security requires development and implementation of neoteric monitoring and control facilities in conditions of increasing speed and intensity of the train movement and high level of terrorist threat. Use of Earth remote sensing (ERS), permitting to obtain information from large areas with a sufficiently high resolution, can provide significant assistance in solving the mentioned problems. This paper discusses the possibility of using various means of remote sensing such as satellites and unmanned aerial vehicles (UAV), also known as drones, for receiving information in different ranges of the electromagnetic spectrum. The paper states that joint using of both these means gives new possibilities in improving railroad security.
文摘为了实时识别快速路交织区拥堵瓶颈的形成及其诱发因素,基于无人机航拍视频构建车辆轨迹数据,提出一种融合交通流不稳定性分析的交织区拥堵识别方法。识别方法由车辆轨迹提取、扰动感知模型和拥堵风险指数构建3个阶段构成。首先,通过YOLOv4(You Only Look Once,Version 4)网络训练航拍小目标权重检测俯拍车辆,关联外观与运动特征以跟踪车辆轨迹,从而提取无人机航拍视频中的精细车辆轨迹。然后,通过提取车辆微观速度、变道、冲突信息建立车速扰动和变道交织扰动感知模型。最后,采用熵值法结合扰动信息与平均车速构建归一化的拥堵风险指数,根据交织流的拥堵风险指数识别拥堵。本文采集广州大桥数据进行案例分析与测试验证。研究结果表明:学习了小目标特征的网络在航拍场景测试的误检率和少检率均低于5%,所提取的车辆轨迹连续稳定;在交织区拥堵识别评价中,本文方法的F1值达到97.85%,明显优于基本参数识别方法,在各路段中具有较高的识别准确度和算法鲁棒性;相比平均速度指标,所提出的拥堵风险指数能够更精细灵敏地反映短时和局部的拥堵,并能够从平均车速、个体车速差异和变道交织3个维度中识别多种因素引起的交织区交通瓶颈。研究结果可为城市重点路段交通诱导与优化提供技术基础。