| In recent years,with the rapid growth of the number of motor vehicles and the continuous expansion of urban scale,the process of urbanization has brought a series of problems and challenges,such as urban pollution,energy loss,unreasonable urban planning,traffic congestion,etc.,in which traffic congestion is a problem for urban development.Especially important,it has become an important problem that researchers need to solve at this stage.The continuous influx of urban population has caused serious traffic congestion.Research on the traffic conditions of single road or independent intersection can not reflect the congestion of urban traffic.The analysis of traffic congestion in multi network network has caused more attention and research by researchers.The urban congestion area usually refers to the areas with more developed business,more residents’ travel times and larger traffic flow,to a certain extent,it is the product of the gathering of people in a short time.Therefore,the mining of the crowd pattern has also become a research indicator of regional traffic congestion detection.Aggregation mode refers to a group of mobile objects moving together under a certain time and space constraints.The emergence of urban traffic clustering patterns often implies the occurrence of abnormal events,such as celebrations,parades,protests and other behavioral patterns,which lead to traffic jams.Compared to the general traffic congestion analysis method,aggregation model congestion analysis method is more intensive,and increases the accuracy and stability at the same time.For more detailed time separation,the incremental aggregation mining method can be used to approximate real-time traffic congestion detection,for urban management and regulation.Make a contribution.This paper first introduces the background and significance of the research,the current research status at home and abroad as well as the main contents of this paper,and expounds the related theories used in this paper.Secondly,the main problems in the mobile location data are analyzed,and the corresponding data preprocessing is carried out in view of the existing trajectory noise and other problems.The trajectory simplification filtering method is introduced to filter the original trajectory before the aggregation mode mining,and the overall efficiency of the aggregation mode mining is improved.One step is to speed up the running time of clustering pattern mining.An improved aggregation mining algorithm is proposed.In the process of closed crowd detection,boundary filtering is added to speed up the computing speed of the distance between clusters.The traditional detection and segmentation(TAD)algorithm is improved in the aggregation detection stage,and a discovery segmentation(FAD)algorithm is proposed.Accelerate the aggregation detection.In order to solve the computing problem of the number of objects appearing in the cluster in the process of long period aggregation detection,a multi bit storage structure based on long integer array is proposed to store binary numeric 0 and 1,and then the number of participants in the aggregation mining is completed,and then the improved aggregation algorithm is used.The status of congestion in urban urban urban areas is analyzed,and the advantages of the traffic congestion analysis are highlighted by the contrast with the dense area which is generated.Finally,a regional traffic congestion visualization system is designed and realized by the incremental aggregation mining algorithm combined with historical gathering data.In Chengdu City,the congestion area of Chengdu City is displayed and the contiguous time aggregation model is added to the display page to find the change of the congestion area with time,and to complete the three-dimensional analysis of the urban traffic situation. |
