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研究生:黃文浩
研究生(外文):Wong, Mun-Hou
論文名稱:以深度學習與週期性樣式探勘為基礎之使用者地點長期預測
論文名稱(外文):Long-Term User Location Prediction Using Deep Learning and Periodic Pattern Mining
指導教授:曾新穆曾新穆引用關係
指導教授(外文):Tseng, Vincent Shin-Mu
口試委員:曾新穆彭文志黃俊龍英家慶
口試委員(外文):Tseng, Vincent Shin-MuPeng, Wen-ChihHuang, Jiun-LongYing, Jia-Ching
口試日期:2017-07-24
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊科學與工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:42
中文關鍵詞:長期預測位置預測軌跡探勘移動模式
外文關鍵詞:Long-Term PredictionLocation PredictionTrajectory MiningMobility Pattern
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近年來,隨著行動通訊技術的進步和第四代行動網絡的發展和日益普及與位置定位技術的發展,行動通設備已經產生了有關人類、車輛、動物等大量的移動軌跡數據,反映出相關物體的移動性,許多新創團隊亦透過預測用戶的下一個位置提供了新穎的服務。目前現有的研究只能預測用戶的下一個位置,也就是是短期位置預測,但是卻無法運用於長期位置預測之情境。因此,本論文主旨為發展出一套長期位置預測的架構與方法。我們認為,如果我們能夠提高長期位置預測的可靠性,目前依賴短期位置預測的服務可以受益,甚至可誕生出更多創新且獨特的服務。在本文中,我們提出了一個基於深度學習和週期性樣式探勘的架構與方法進行長期位置預測。我們的預測架構與方法套用了自然語言模型的想法,並採用多步遞歸策略以進行長期預測。為了減少多步遞歸策略所的累積的誤差損失,我們利用週期性樣式探勘技術,減少所需的遞歸次數,進而減少損失,提高預測架構與方法的可靠性。基於真實世界的移動軌跡數據,我們進行了一系列的實驗。實驗結果顯示,本研究所提出的預測架構與方法可以做出有效的長期位置預測。
In recent years, with the advances in mobile communication and growing popularity of the fourth-generation mobile network along with the enhancement in location positioning techniques, mobile devices have generated extensive spatial trajectory data, which represent the mobility of moving objects. New services are emerged to serve mobile users based on their predicted locations. This thesis is concerned with the long-term location prediction of a user. Most of the existing studies on location prediction can only predict one next location of a user, which is regarded as short-term next location prediction, and they are not applicable for long-term location predictions. We believe that if we can improve the accuracy of long-term next location prediction predict, every current service that takes benefits of predictability on next location can be further extended. In this thesis, we propose a prediction framework named LSTM-PPM that utilises deep learning and periodic pattern mining for effective long-term location prediction. Our framework devises the ideology from natural language model and uses multi-step recursive strategy to perform long-term prediction. To reduce the accumulated loss in multi-step strategy, we utilise further the periodic pattern mining technique. Through empirical evaluation on a real-life trajectory data, our framework is shown to provide effective performance in long-term location prediction.
摘要 .............................................................................................................................................i
Abstract .....................................................................................................................................ii
誌謝 ...........................................................................................................................................iii
Table of Contents.....................................................................................................................iv
List of Figures ..........................................................................................................................vi
List of Tables..........................................................................................................................viii
Chapter 1 Introduction ............................................................................................................1
1.1 Background .................................................................................................................. 1
1.2 Motivation.................................................................................................................... 1
1.3 Research Aims and Challenges ..................................................................................... 2
1.4 Thesis Organisation ...................................................................................................... 3
Chapter 2 Related Works ........................................................................................................4
2.1 Significant Location Detection...................................................................................... 4
2.2 Mobility Model............................................................................................................. 5
Chapter 3 The Proposed Framework .....................................................................................9
3.1 Dataset Preprocessing................................................................................................. 10
3.1.1 Indoor Filling ....................................................................................................... 10
3.1.2 Stay Point Detection............................................................................................. 11
3.1.3 Stay Point Clustering............................................................................................ 13
3.1.4 Place Semantic Inferring ...................................................................................... 14
3.1.5 Day Sequence Generation .................................................................................... 15
v
3.1.6 Periodic Pattern Mining........................................................................................ 16
3.2 Training the Model ..................................................................................................... 16
3.2.1 The Recurrent Neural Network............................................................................. 16
3.2.2 Design Logic and Implementation Details ............................................................ 20
Chapter 4 Experimental Evaluation.....................................................................................25
4.1 Data Description......................................................................................................... 25
4.2 Experiment Settings.................................................................................................... 25
4.3 Comparison Targets and Metrics ................................................................................ 26
4.4 Experimental Results .................................................................................................. 27
4.4.1 Short-Term Prediction Experiments ..................................................................... 27
4.4.2 Long-term Prediction Experiments ....................................................................... 31
4.5 Discussion .................................................................................................................. 34
Chapter 5 Conclusion and Future Works ............................................................................35
5.1 Conclusion ................................................................................................................. 35
5.2 Future Works.............................................................................................................. 36
Bibliography............................................................................................................................37
摘要 i
Abstract ii
誌謝 iii
Table of Contents iv
List of Figures vi
List of Tables viii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 1
1.3 Research Aims and Challenges 2
1.4 Thesis Organisation 3
Chapter 2 Related Works 4
2.1 Significant Location Detection 4
2.2 Mobility Model 5
Chapter 3 The Proposed Framework 9
3.1 Dataset Preprocessing 10
3.1.1 Indoor Filling 10
3.1.2 Stay Point Detection 11
3.1.3 Stay Point Clustering 13
3.1.4 Place Semantic Inferring 14
3.1.5 Day Sequence Generation 15
3.1.6 Periodic Pattern Mining 16
3.2 Training the Model 16
3.2.1 The Recurrent Neural Network 16
3.2.2 Design Logic and Implementation Details 20
Chapter 4 Experimental Evaluation 25
4.1 Data Description 25
4.2 Experiment Settings 25
4.3 Comparison Targets and Metrics 26
4.4 Experimental Results 27
4.4.1 Short-Term Prediction Experiments 27
4.4.2 Long-term Prediction Experiments 31
4.5 Discussion 34
Chapter 5 Conclusion and Future Works 35
5.1 Conclusion 35
5.2 Future Works 36
Bibliography 37
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