本文選題：電動自行車數(shù)據(jù) + 居住地挖掘��； 參考：《浙江大學》2017年碩士論文

【摘要】：近年來,電動自行車行業(yè)迅速發(fā)展,很大程度上改善了居民出行條件,提高了出行效率。但與之相關的交通問題、社會治安問題也日益凸顯,例如擾亂交通秩序、引發(fā)交通事故、車輛頻繁被盜等。為了解決電動自行車發(fā)展帶來的一系列問題,管理部門需要全面深入地了解該群體的行為特征,以加強對電動自行車的管理,使其更好地服務于社會經濟的發(fā)展。當前,我國部分城市安裝了電動自行車智能防盜追蹤系統(tǒng),采集了大量的電動自行車實時位置信息,使我們可以從海量數(shù)據(jù)出發(fā),利用大數(shù)據(jù)相關技術,挖掘其中蘊含的有價值信息,為電動自行車管理提供指導建議。本文基于我國某城市的電動自行車數(shù)據(jù)開展了系統(tǒng)性研究工作,對電動自行車用戶的行為特征進行深入分析,并構建了功能豐富的可視化交互系統(tǒng)。具體貢獻如下:本文基于電動自行車數(shù)據(jù)提取停留地點和移動軌跡,建立并實現(xiàn)了基于分層模型的用戶居住地自動挖掘算法。為分析用戶對不同停留地點的訪問規(guī)律,采用停留地點偏好矩陣建模用戶的日程訪問行為,并提出一種度量用戶日程訪問行為相似性的方法,在此基礎上建立Kmeans用戶聚類模型,采用DB Index確定最優(yōu)聚類類別。實驗表明模型可有效地區(qū)分用戶行為規(guī)律,幫助人們了解其生活習慣與職業(yè)背景分布。為利用電動自行車移動軌跡估算電動自行車騎行速度,本文針對現(xiàn)有道路匹配算法不能解決采樣頻率低、空間相關性弱的電動自行車數(shù)據(jù)的道路匹配問題,設計了一種基于路徑約束的道路匹配算法。該算法通過多個數(shù)據(jù)間的空間聯(lián)系確定行駛路徑,并綜合利用kd樹、A*搜索算法和動態(tài)規(guī)劃方法,在提高匹配準確性的同時保證了較高的計算效率。在數(shù)據(jù)分析的基礎上,本文還構建了Web端的可視化系統(tǒng),方便人們直觀地分析和理解電動自行車數(shù)據(jù)。該系統(tǒng)包括電動自行車監(jiān)控、移動性分析和用戶行為分析等多種功能,為本文研究工作的實際應用提供了可能。
[Abstract]:In recent years, the rapid development of electric bicycle industry has greatly improved the travel conditions of residents and increased travel efficiency. However, the related traffic problems and social security problems have become increasingly prominent, such as disturbing traffic order, causing traffic accidents, frequent theft of vehicles and so on. In order to solve a series of problems brought about by the development of electric bicycle, the management department should fully and deeply understand the behavior characteristics of this group, in order to strengthen the management of electric bicycle and make it serve the development of social economy better. At present, some cities of our country have installed the intelligent anti-theft tracking system of electric bicycle, and collected a large amount of real-time location information of electric bicycle, so that we can start from the massive data and make use of the related technology of big data. Excavate the valuable information contained therein, provide the guidance suggestion for the electric bicycle management. Based on the data of electric bicycle in a certain city of our country, this paper carries out systematic research work, deeply analyzes the behavior characteristics of electric bicycle users, and constructs a visual interactive system with rich functions. The main contributions are as follows: based on the data of electric bicycle, an automatic mining algorithm of user residence based on hierarchical model is established and implemented in this paper. In order to analyze the rules of users' access to different places of stay, this paper presents a method to measure the similarity of user's visit behavior of schedule, and then establishes a Kmeans clustering model. DB Index is used to determine the optimal clustering category. Experiments show that the model can effectively distinguish user behavior and help people understand their living habits and professional background distribution. In order to estimate the cycling speed of electric bicycle by using the moving track of electric bicycle, the existing road matching algorithm can not solve the road matching problem of the data of electric bicycle with low sampling frequency and weak spatial correlation. A path matching algorithm based on path constraint is designed. The algorithm determines the driving path by the spatial connection of multiple data, and synthetically uses the KD tree search algorithm and dynamic programming method to improve the accuracy of the matching and ensure a high computational efficiency. On the basis of data analysis, this paper also constructs a visual system of Web side, which is convenient for people to analyze and understand the data of electric bicycle intuitively. The system includes many functions, such as electric bicycle monitoring, mobility analysis and user behavior analysis.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U491.225;TP311.13

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本文編號：2057025