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Mining And Application Research Of Students' Movement Trajectory In Campus Environment

Posted on:2022-10-13Degree:MasterType:Thesis
Country:ChinaCandidate:H ChenFull Text:PDF
GTID:2517306755951189Subject:Electronics and Communications Engineering
Abstract/Summary:PDF Full Text Request
In recent years,with the rapid development of big data acquisition technology,data mining has become more and more widely used in various fields.In daily life,people's travel will produce a large amount of trajectory information,which contains rich behavior patterns.By mining trajectory data,it can provide users with more personalized services.The research in this paper is based on the application scenarios of college students' daily travel,according to the characteristics of the students' own behavior,mining the students' trajectory data and analyzing their travel rules.In this paper,the following researches are done on data preprocessing,stop point recognition,and frequent pattern mining:(1)Introduce the basic knowledge of trajectory data and analyze the key technologies that need to be used in trajectory mining.(2)The preprocessing algorithm of the student's movement trajectory is given.Based on the analysis of the trajectory data collection point and its GPS data,the trajectory data is coordinated to filter drift and abnormal data;finally,the missing movement trajectory is performed Recognize and fill up to get the corrected trajectory data.(3)Identify and extract the stay points in the trajectory data,compare the stay point recognition methods,and select the improved TS-DBSCAN clustering algorithm for recognition.The stay points in the cluster are obtained and combined with time information to extract the time series distribution The stay point is to provide data support for mining students' daily travel patterns.Then the obtained stay points are semantically made,and combined with the campus architecture and the characteristics of student travel,the stay points are classified into the different types of student stay activities.(4)Define the travel patterns of students in the campus environment,study the trajectory data of students for a month,analyze their behavior trajectories,compare frequent travel patterns with frequent sequential behavior patterns,and perform visual chart analysis.Find out the student's travel pattern.
Keywords/Search Tags:Frequent trajectory patterns, Stop point recognition, Behavior trajectory analysis, Trajectory mining, Data visualization
PDF Full Text Request
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