Coordinated Control Of Urban Traffic Based On Spatial And Temporal Characteristics

With the continuous development of city in our country,the city car ownership increasing year by year,the city space resources available less and less,the road traffic developing unbalance of supply and demand is increasingly prominent,such as traffic congestion problem is increasingly serious.The development of information technology,the application of cloud computing,big data and intelligent detection technology in the traffic field,making traffic flow data acquisition and application more convenient.In the urban traffic network,due to the traffic flow itself has obvious space-time characteristic,considering the influence of the space-time characteristic of traffic flow,and using the intelligent algorithm to fusion the spatiotemporal data,obtain the comprehensive and reliable traffic flow data.It is of great practical significance to reduce traffic congestion through the spatial and temporal characteristics of urban traffic synergy control,the small scale or node flow control in time,and the reasonable distribution of traffic flow in space.In this thesis,based on the application of spatio-temporal characteristics in traffic field and the research status of traffic coordination control,this paper analyzed the coordinated control of urban traffic with temporal and spatial characteristics,including the analysis of traffic flow parameters,the selection of traffic flow parameters,the spatial and temporal characteristics of traffic and the correlation of spatio-temporal data.Aiming at the intelligent traffic algorithms related to traffic cooperative control,such as genetic algorithm and particle swarm optimization algorithm are analyzed and expounded.The traffic data of the time series focuses on the change of traffic flow at the section,while the spatially sequenced traffic data mainly describe the distribution of traffic flow on the road network.Analyze traffic data with time characteristics,spatial characteristics,and spatio-temporal characteristics,and use space-time correlation coefficients to analyze the interaction between spatio-temporal data.Before the data operation,the traffic data is pre-processed to obtain reliable traffic data of space-time sequence.Considering the temporal and spatial characteristics,the GA-WNN traffic data fusion model is established by using the genetic algorithm group search technology and the strong learning ability of the wavelet neural network,and the MAE,MRE and MSE are used.The evaluation indicators analyze the spatial-temporal traffic data sequence to obtain a more comprehensive,more comprehensive and effective spatial-temporal data sequence.Finally,the modeling method of the urban traffic cooperative control model with spatio-temporal characteristics is analyzed,including the basic theory of cooperative control,collaborative control target,and the impact parameters of time-space saturation,delay of continuous intersection,travel time and so on to synergetic control.Based on the constraints of time-space saturation and intersection delay,a cooperative control model is established to minimize the travel time and to solve the section congestion.Using genetic algorithm-optimized particle swarm algorithm to improve the segmentation strategy of the population,improve the global optimal ability of the particle swarm algorithm and improve the search accuracy,solving the spatio-temporal characteristics of traffic cooperative control model.Through an example analysis,spatio-temporal traffic coordination control can effectively shorten travel time,optimize traffic control parameters and balance traffic distribution,and achieve the purpose of alleviating the traffic congestion.