Optimization model of bayonet layout for individual vehicle travel detection
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 se-quences,the methods of first reconstruction and second reconstruction of missing trajectory were pro-posed.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 con-straints,and maximizing the dispersion of missing tracks as the optimization objective of bayonet lay-out.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 ana-lyzed 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 dis-persion of missing track by 139%,which obtained better results than the current scheme in terms of in-dividual 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.
urban trafficbayonet layoutparticle swarm optimization(PSO)individual travel detec-tiondispersion of missing trajectory
国家科技期刊平台
NSTL
万方数据
维普
