摘要
针对高清卡口检测器可以获取出行者个体级别的细粒度信息但难以实现路网全覆盖的问题,从缺失轨迹重构角度出发,研究了面向车辆个体出行检测的卡口布设优化方法。考虑相邻两次卡口检测序列间的缺失情况,提出缺失轨迹一次重构、二次重构方法。基于一次重构原理,创建流量捕获率、轨迹覆盖率以衡量卡口布设方案对路面交通信息的检测规模;基于二次重构原理,创建缺失轨迹离散度以衡量轨迹重构可靠度。以流量捕获率、轨迹覆盖率作为约束条件,以最大化缺失轨迹离散度作为卡口布设优化目标,构建了面向车辆个体出行检测的卡口布设优化模型,并采用粒子群算法求解。以广州市海珠区某区域路网为例,分全新布设和新增布设两种情况进行算例分析,结果表明:在全新布设场景中,优化后的卡口布设方案相流量捕获率提升了6.20%,轨迹覆盖率提升了2.76%,缺失轨迹离散度提升了139%,在车辆个体出行轨迹检测及重构方面比当前方案获得了更优的效果;在新增布设场景中,依次对新增的1-6个卡口进行优化求解,得到了新增位置和优化结果。
Bayonet detector can obtain fine-grained information at individual traveler level,but it is difficult to achieve full coverage of the road network.Aiming at this problem,from the perspective of missing trajectory reconstruction,the bayonet layout optimization method for individual vehicle travel detection was studied.Considering the missing situation between two adjacent bayonet detection sequences,the methods of first reconstruction and second reconstruction of missing trajectory were proposed.Based on the principle of primary reconstruction,the flow capture rate and track coverage rate were created to measure the monitoring scale of road traffic information by bayonet layout scheme.Based on the principle of secondary reconstruction,the missing trajectory dispersion was created to measure the reliability of trajectory reconstruction.A bayonet layout optimization model for individual vehicle travel detection was constructed by taking traffic capture rate,trajectory coverage as constraints,and maximizing the dispersion of missing tracks as the optimization objective of bayonet layout.Particle swarm optimization algorithm was used to solve the optimization model.Taking a regional road network in Haizhu,Guangzhou as an example,the new layout and the added layout were analyzed respectively.The results showed that:in the new layout scene,the optimized bayonet layout scheme increased the phase flow capture rate by 6.20%,the track coverage rate by 2.76%,and the dispersion of missing track by 139%,which obtained better results than the current scheme in terms of individual vehicle travel track detection and reconstruction.In the added layout scene,the optimization solution of added 1-6 bayonets were carried out successively,and the added positions and optimization results were obtained.
作者
乔文瑛
黄敏
张小兰
QIAO Wenying;HUANG Min;ZHANG Xiaolan(School of Intelligent Systems Engineering,Sun Yat-sen University,Shenzhen 518107,China;Guangdong Provincial Key Laboratory of Intelligent Transportation System,Shenzhen 518107,China;Guangdong Provincial Engineering Research Center for Traffic Environmental Monitoring and Control,Shenzhen 518107,China;Guangdong Polytechnic of Industry and Commerce,Guangzhou 510510,China)
出处
《中山大学学报（自然科学版）（中英文）》
CAS
CSCD
北大核心
2024年第1期137-144,共8页
Acta Scientiarum Naturalium Universitatis Sunyatseni
基金
国家重点研发计划(2020YFB1600400)。
关键词
城市交通
卡口布设
粒子群算法(PSO)
个体出行检测
缺失轨迹离散度
urban traffic
bayonet layout
particle swarm optimization(PSO)
individual travel detection
dispersion of missing trajectory
