Based On The Space-time Cube Of Traffic Jams Point Temporal-spatial Pattern Mining And Analysis

With the acceleration of urbanization and the popularization of motor vehicles,the contradiction between traffic supply and demand is becoming more and more prominent,and the development of intelligent traffic system,traffic management and information service capability has become an effective way to solve the traffic jam problem.The world's governments have been actively developing intelligent traffic system,intelligent traffic platform continues to emerge,resulting in the rapid growth in data traffic,but the effective use of the data is very small,there is a data explosion but the lack of knowledge of the phenomenon.Nowadays,data mining technology is becoming more and more mature.Data mining technology has powerful and flexible analysis and processing capability for big data.It is suitable for mining the implicit information in big data.The data mining technology is introduced into the traffic big data analysis is also the trend of the development of the intelligent transportation.For that reason,the paper based on the space-time cube model of temporal-spatial hot spot analysis method of exploratory research,aims to build a suitable for traffic information pattern mining scheme of space and time.First of all,it is difficult to collect traffic jam data.In this paper,using network crawler,image processing and spatial database technology,it's designed that a real-time traffic collection,storage and management system based on network map.it provides traffic jams data to temporal-spatial hot spot mining and analysis.Considering the traffic jams point contains time,space and attribute information,it belongs to the category of spatio-temporal data.The spatio-temporal pattern mining of traffic jams is needed to consider the spatial and temporal constraints of data.In this paper,time,space and attribute three elements are integrated to model,and the spatial and temporal cube model is used to express its temporal and spatial relation,and the integrity of traffic jams time and spatial information is expressed,and the data model is provided for the visualization of traffic jams.Secondly,based on spatial hot spot analysis and time series analysis,the space-time cube model is used to integrate the spatiotemporal relation into the hotspot analysis,and the hot and cold spots in the spatial and temporal distribution of traffic jams are detected.The time series analysis is used to evaluate the change of these hot and cold spots with temporal trend,the classification of the hot and cold spatial and temporal types.Due to the aggregation of traffic jams at different spatial and temporal scales,different space-time cube of traffic jams points will be build,and the patterns and relationships that are finally excavated may be very different.This paper explores the scale of time-space mining of traffic jams by means of the control factor method.The experimental results show that the traffic jams point will be excavated with the smaller time step and space distance,and the hot and cold points will be excavated.There is a corresponding neighborhood threshold,and the result of mining after reaching the threshold is broadly similar.On the basis of this,using the theory of spatial autocorrelation,time autocorrelation and time period to study the scale selection method of time and space model mining based on traffic jams point.Finally,the urban area of Chengdu is selected as the experimental area,and the temporal-spatial hots pot mining of traffic jams is analyzed.According to the collected traffic flow data,the traffic crossover point of time-space cube is created to express the spatial and temporal distribution of traffic jams.Based on the time-space cube model of traffic jams,the temporal-spatial hot spot distribution model of traffic jams is analyzed.At the same time,the temporal and spatial trend of traffic jams is classified,and the spatial and temporal distribution pattern of traffic jam is presented by visualization of hot and cold map.The experimental results show that the spatial and temporal distribution model of traffic jams in Chengdu can be effectively excavated by using the space-time cube model and the time-space hotspot analysis method.In this paper,the methods and techniques of time-space model mining,including data acquisition and management of traffic jams point,space-time cube model and time-space hotspot analysis.There are certain theory value and application significance on certain aspects in the mining and analysis of time-space distribution pattern of traffic jams.