Analysis Of Highway Travel Characteristics Based On Spatio-temporal Big Data

With the continuous construction of China's highway network and the steady increase in the number of cars,various data collection equipment applied in the field of highways has accumulated a large amount of diverse data.In the face of fast-growing expressway space-time big data,highway traffic capabilities face huge challenges,while traffic management and operations departments need more comprehensive data analysis.In recent years,the analysis of travel characteristics is considered to be a core research content in the field of intelligent transportation,which can provide traffic management departments with data support for traffic guidance and decision-making.Based on spatio-temporal big data technology,this paper analyzes the travel characteristics of the highway field,and realizes the travel trend analysis of road network group users and the individual user user characteristics.The specific research work is as follows:1.Aiming at the analysis of highway user group trends,the insufficient use of the spatiotemporal characteristics of the data leads to problems such as low quality of data analysis and low efficiency of mass data processing.A long-term traffic prediction model based on ensemble learning is proposed.The cleaned charge data,weather data,calendar data,etc.are analyzed in spatio-temporal correlation,and a gradient lift regression model is combined to construct a long-term traffic prediction model based on spatio-temporal big data.After comparative experimental analysis,the toll station vehicle flow trend prediction model proposed in this paper has higher accuracy and efficiency than the traditional time series model,and uses toll stations as a unit to predict the travel trends of the entire network of group users.2.Aiming at the analysis of individual user characteristics of expressways,the lack of utilization of travel history behaviors with spatio-temporal attributes results in low accuracy mining of potential user characteristics,and a user characteristic analysis method based on user classification is proposed.By constructing highway user travel trajectories,analyzing user travel characteristics,and constructing a user classification model based on ensemble learning.After experimental verification,the user classification model proposed in this paper has higher accuracy and efficiency than the traditional classification model,and according to the results of user classification,the potential characteristics of individual users are mined.3.Aiming at the problem of lack of spatio-temporal big data technology application for business analysis in the field of highway,a highway characteristic analysis system based on spatio-temporal big data technology was implemented.Utilizing massive spatio-temporal data of expressways,the travel feature analysis system displays the travel characteristics analysis results based on spatiotemporal big data such as group user travel trends,individual user travel trajectories,and individual user characteristics.Provide a reliable and effective traffic condition monitoring platform for the traffic management department.