| DC 欄位 |
值 |
語言 |
| DC.contributor | 資訊工程學系 | zh_TW |
| DC.creator | 王勤 | zh_TW |
| DC.creator | Qin Wang | en_US |
| dc.date.accessioned | 2017-6-28T07:39:07Z | |
| dc.date.available | 2017-6-28T07:39:07Z | |
| dc.date.issued | 2017 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=105522088 | |
| dc.contributor.department | 資訊工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 車輛的軌跡數據集通常受到GPS誤差跟低樣本比率影響。因此，在將其用於任何研究或應用之前，必須清理軌跡數據集。在本文中，我們提出了一個基於HMM的系統來清理軌跡數據集和重建車輛的缺失行駛路線。考慮到每筆資料的可能的道路是會改變的，並提出了一組轉移概率和觀察概率的公式。實驗使用OpenStreetMap的北京圖資和Microsoft Research Lab, Asia收集的計程車軌跡數據集，證實了我們提出的系統顯著提高了數據集的質量。 | zh_TW |
| dc.description.abstract | The vehicle trajectory dataset is often contaminated by GPS errors and low sampling rate. Consequently, it is important to cleanse the trajectory dataset before it can be used for any research or application. In this paper, we propose a HMM based system to cleanse and rebuild the missing traveling routes of vehicles in a given trajectory dataset. Considering the candidates of each entry as variables, a set of formulae for the transition probability and observation probability are proposed. The experiments using the OpenStreetMap of Beijing and the taxi trajectory dataset collected by Microsoft Research Lab, Asia confirm that our proposed system significantly improves the quality of the dataset. | en_US |
| DC.subject | Hidden Markov model | zh_TW |
| DC.subject | HMM | zh_TW |
| DC.subject | trajectory data cleansing | zh_TW |
| DC.subject | Hidden Markov model | en_US |
| DC.subject | HMM | en_US |
| DC.subject | trajectory data cleansing | en_US |
| DC.title | Trajectory Data Cleansing Using HMM | en_US |
| dc.language.iso | en_US | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |