摘要
将无人飞机技术应用到稀疏路网的交通监控当中,提出了无人飞机在有无续航里程约束条件下的交通监控部署方法。给出了无人飞机的监控路段和节点的选择方法;在无续航里程约束条件下,将无人飞机的交通监控问题转化为旅行商问题,并运用模拟退火算法予以求解;在有续航里程约束条件下,运用K-means聚类方法,将无人飞机的监控区域划分成若干子监控区,从而将该问题转化为无续航里程约束的无人飞机交通监控部署问题。以新疆库库高速公路和路网为例,对稀疏路网条件下的无人飞机交通监控部署方法进行实证分析和试飞试验。试验结果表明:无人飞机在稀疏路网条件下是一种有效的交通监控设备,可用于我国西部稀疏路网的交通监控。
Unmanned Aerial Vehicle（UAV） technology was introduced into the traffic surveillance in sparse road network and an UAV allocation method for traffic surveillance with/without UAV continuous flight distance constraint was proposed.First,the method of choosing the surveillance road segments and nodes was proposed.Then,UAV traffic surveillance problem without continuous flight distance constraint was formulated as a traveling salesman problem,and the simulated annealing algorithm was introduced to solve this problem.As for UAV traffic surveillance problem with continuous flight distance constraint,K-means clustering algorithm was used to divide the UAV surveillance area into multiple sub-zones to convert this problem into UAV traffic surveillance scenario without continuous flight distance constraint.Finally,taking Korla-Kuqa expressway of Xinjiang and its road network as example,the proposed UAV-based traffic surveillance allocation method for sparse road network was demonstrated and validated by using several field experiments.The experimental results show that UAV is an effective and useful tool for traffic surveillance in sparse road network of China western regions.
出处
《公路交通科技》
CAS
CSCD
北大核心
2012年第3期124-130,共7页
Journal of Highway and Transportation Research and Development
基金
国家高技术研究发展计划(八六三计划)项目(2009AA11Z220)
关键词
交通工程
稀疏路网
模拟退火算法
K-means聚类法
无人飞机部署
交通监控
traffic engineering
sparse road network
simulated annealing algorithm
K-means clustering algorithm
unmanned aerial vehicle allocation
traffic surveillance
