为提高交通感知网络的监测效率和高速公路突发事件下的应急救援效率,将无人机空中传感器作为道路交通流检测设备,通过合理布设规划空中传感器布设位置实时道路交通流监测状态。融合事故数据和流量数据,基于信息熵理论构建路段候选点重...为提高交通感知网络的监测效率和高速公路突发事件下的应急救援效率,将无人机空中传感器作为道路交通流检测设备,通过合理布设规划空中传感器布设位置实时道路交通流监测状态。融合事故数据和流量数据,基于信息熵理论构建路段候选点重要度模型,进而量化高速公路路段的巡检优先级。在此基础上,以最大化覆盖重要路段为目标,以传感器布设原则和预算上限为约束,构建了空中传感器布设优化模型,并通过对比分析不同预算上限的空中传感器布设方案以及视频传感器布设方案,验证了模型的有效性。结果表明:相较于视频传感器布设方案,空中传感器布设能够有效降低传感器布设成本、提高监测范围的空间覆盖率。To improve the monitoring efficiency of the traffic perception network and the emergency rescue efficiency under highway emergencies, the UAV aerial sensor was used as the road traffic flow detection equipment, and the real-time road traffic flow monitoring status was planned through a reasonable layout planning of the aerial sensors’ laying position. Accident and traffic data are fused, and the importance model of candidate points of road sections is constructed based on the information entropy theory. Then, the inspection priority of highway sections is quantified. On this basis, to maximize the coverage of important road sections, an aerial sensor deployment optimization model is constructed based on the constraint of sensor deployment principle and budget upper limit, and the effectiveness of the model is verified by comparing and analyzing the aerial sensor deployment scheme and video sensor deployment scheme with different budget upper limits. The results show that compared with the video sensor deployment scheme, the aerial sensor deployment can effectively reduce the sensor deployment cost and improve the spatial coverage of the monitoring range.展开更多
文摘为提高交通感知网络的监测效率和高速公路突发事件下的应急救援效率,将无人机空中传感器作为道路交通流检测设备,通过合理布设规划空中传感器布设位置实时道路交通流监测状态。融合事故数据和流量数据,基于信息熵理论构建路段候选点重要度模型,进而量化高速公路路段的巡检优先级。在此基础上,以最大化覆盖重要路段为目标,以传感器布设原则和预算上限为约束,构建了空中传感器布设优化模型,并通过对比分析不同预算上限的空中传感器布设方案以及视频传感器布设方案,验证了模型的有效性。结果表明:相较于视频传感器布设方案,空中传感器布设能够有效降低传感器布设成本、提高监测范围的空间覆盖率。To improve the monitoring efficiency of the traffic perception network and the emergency rescue efficiency under highway emergencies, the UAV aerial sensor was used as the road traffic flow detection equipment, and the real-time road traffic flow monitoring status was planned through a reasonable layout planning of the aerial sensors’ laying position. Accident and traffic data are fused, and the importance model of candidate points of road sections is constructed based on the information entropy theory. Then, the inspection priority of highway sections is quantified. On this basis, to maximize the coverage of important road sections, an aerial sensor deployment optimization model is constructed based on the constraint of sensor deployment principle and budget upper limit, and the effectiveness of the model is verified by comparing and analyzing the aerial sensor deployment scheme and video sensor deployment scheme with different budget upper limits. The results show that compared with the video sensor deployment scheme, the aerial sensor deployment can effectively reduce the sensor deployment cost and improve the spatial coverage of the monitoring range.
