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研究生(外文):Chia-Chun Weng
論文名稱(外文):Visiting Trip Planning with Moving Time Minimization in Indoor Public Spaces Based on Internet of Things Technologies
指導教授(外文):Lien-Wu Chen
口試委員(外文):Shiow-Fen HwangChi-Fu Huang
外文關鍵詞:Crowd DensityDeep LearningTrip PlanningIndoor PositioningInternet of Things
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Internet of Things (IoT) localization technologies employ RFID, iBeacon, and sensors to obtain the crowd information of indoor people. The crowd information includes the number of indoor people, moving speeds, walking directions, and user density in specific areas.
In this work, iBeacon IoT devices are deployed in the indoor space, and Pedestrian Dead Reckoning through the smartphone accelerometer and gyroscope is used to derive continuous user locations based on occasional iBeacon positions. The user position, walking direction, and traveling distance can form a moving trajectory. We propose a visiting trip planning to plan the most efficient visiting order and the fastest walking paths. The proposed framework consists of a deep learning based crowd density prediction model and a time-dependent trip planning algorithm. The open crowd dataset of the Osaka Asia & Pacific Trade Center in Japan are used to evaluate the performance of existing works and ours.
Experimental results show that our framework outperforms existing methods and can accurately predict the future crowd density of indoor people. In particular, our framework determines the optimal visiting order of target places based on the predicted crowd density and can significantly reduce the total moving time in the entire visiting trip with multiple target places.

誌謝 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 v
表目錄 vi
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 論文架構 3
第二章 相關研究 4
2.1 空間預測模型(Spatial Prediction Model) 5
2.2 拜訪行程規劃(Visiting Trip Planning) 6
第三章 系統架構 9
第四章 拜訪行程規劃解決方案 11
4.1 密集卷積網路 11
4.2 人潮密度預測 14
4.2.1數據處理層 14
4.2.2密集卷積層 17
4.2.3時空融合層 18
4.3 跨時段行程規劃 20
第五章 效能評估 24
5.1 人潮預測效能分析 24
5.2 跨時段拜訪行程效能分析 26
第六章 結論 30
參考文獻 31

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