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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93348完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 徐宏民 | zh_TW |
| dc.contributor.advisor | Winston H. HSU | en |
| dc.contributor.author | 林忠毅 | zh_TW |
| dc.contributor.author | Chung-Yi Lin | en |
| dc.date.accessioned | 2024-07-29T16:23:02Z | - |
| dc.date.available | 2024-07-30 | - |
| dc.date.copyright | 2024-07-29 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-26 | - |
| dc.identifier.citation | Reference
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93348 | - |
| dc.description.abstract | 交通預測對於現代城市規劃和智慧交通系統（ITS）至關重要，因為它在交通管理、資源分配和公共安全方面具有顯著的優勢。然而，傳統方法依賴部署實體的感測器和攝影機，成本高且覆蓋範圍不足，突顯了需要可擴展且高效的替代方案。為了解決這些挑戰，我們探索了電信數據作為交通預測的彈性且可擴展的替代方案潛力。
我們提出使用從各個地區收集的“基於電信的流量”來評估交通狀況並同時保護用戶隱私。然後，我們提出一個時空預測模型來基於電信流量來預測這些區域未來的變化，作為初步的交通指標。為了有效地提高預測準確性，我們提出了一個多模態數據融合框架，利用廣泛電信數據與稀疏基於影像數據的結合來改善預測效果。 進一步，為了解決電信流量與實際交通流量之間的差距，我們改進了融合框架並提出聯合損失函數，使預測結果對齊實際交通流量。此外，我們開發了新穎框架，透過跨模態方式將無方向性的電信數據轉為有方向性的移動流量，解決缺乏方向性的問題。這些方法確保了更準確的實際交通狀況、精確的交通動態，並為城市管理提供了寶貴的見解。 總體而言，本論文通過將電信數據與交通領域知識相結合，推進了交通預測的發展。我們的創新方法提供了多樣且可擴展的解決方案，超越了傳統基於傳感器的系統，並為城市規劃和智慧交通系統做出了重大貢獻。 | zh_TW |
| dc.description.abstract | Traffic prediction is crucial for modern urban planning and intelligent transportation systems (ITS) due to its significant benefits for traffic management, resource allocation, and public safety. Traditional methods, reliant on fixed-location sensors and cameras, are costly and provide insufficient coverage, highlighting the need for scalable and efficient alternatives. To address these challenges, we explore the potential of telecom data as a flexible and scalable source for traffic prediction.
We propose the use of "telecom-based flow" collected from various areas to evaluate traffic conditions while protecting data privacy. We then present a spatio-temporal predictive model to predict future traffic conditions as an initial traffic indicator. To further improve prediction accuracy, we propose a multi-modal data fusion framework that combines extensive telecom data with sparse vision-based data. To address the gap between telecom-based and actual traffic flow, we develop and enrich the fusion framework with a joint loss function to more closely align predictions with actual traffic flow. Additionally, we develop a cross-modal framework to convert undirected telecom data into directed mobility flow to address the lack of directionality. These approaches ensure more accurate real-world traffic conditions, precise traffic dynamics, and provide valuable insights for urban management. Overall, this dissertation advances traffic prediction by integrating telecom data with traffic domain knowledge. Our innovative approaches offer diverse and scalable solutions, extending beyond traditional sensor-based systems, and provide significant contributions to urban planning and ITS. | en |
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| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
Acknowledgements iii 摘要 v Abstract vii Contents ix List of Figures xiii List of Tables xix Chapter 1 Introduction 1 Chapter 2 Spatio-Temporal Prediction of Telecom-Based Flows 5 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Multi-type GCT flows Dataset . . . . . . . . . . . . . . . . . . . . . 7 2.2.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.2 Data Collection and Preprocessing . . . . . . . . . . . . . . . . . . 8 2.2.3 Data Analysis of GCT flow . . . . . . . . . . . . . . . . . . . . . . 9 2.2.4 Interactions among GCT Flow’s Types. . . . . . . . . . . . . . . . 11 2.2.5 Path Forward for Real-world Deployment . . . . . . . . . . . . . . 12 2.3 MultiFaceted Graph Modeling (MFGM) . . . . . . . . . . . . . . . . 13 2.3.1 V-GCT Flow Prediction Task Definition . . . . . . . . . . . . . . . 13 2.3.2 Overview of the Proposed Model . . . . . . . . . . . . . . . . . . . 14 2.3.3 Channel-specific Graph Attention Layer (CGATL) . . . . . . . . . 15 2.3.4 Multivariate Facet Modeling . . . . . . . . . . . . . . . . . . . . . 17 2.3.5 Temporal Facet Modeling . . . . . . . . . . . . . . . . . . . . . . . 18 2.3.6 Spatial Facet Modeling . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.7 Overall Structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.4.1 V-GCT Prediction Evaluation . . . . . . . . . . . . . . . . . . . . . 22 2.4.2 Ablation Study of MFGM . . . . . . . . . . . . . . . . . . . . . . . 22 2.4.3 Sensitivity Analysis of Multi-Type GCT Flows . . . . . . . . . . . 23 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Chapter 3 Efficient Spatio-Temporal Graph Neural Network 25 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 Multi-Channel Graph Attention . . . . . . . . . . . . . . . . . . . . 27 3.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Chapter 4 Multi-Modal Data Fusion for Prediction Accuracy 31 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.2 CTCam Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2.1 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.2.2 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2.3 Data Collection and Processing . . . . . . . . . . . . . . . . . . . . 38 4.3 Two-Stage Fusion for Prediction . . . . . . . . . . . . . . . . . . . . 40 4.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.4.1 Experimental Settings and Baselines . . . . . . . . . . . . . . . . . 43 4.4.2 Prediction Improvement . . . . . . . . . . . . . . . . . . . . . . . . 44 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Chapter 5 Multi-Modal Alignment of Telecom Data with Actual Traffic 47 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5.2 Tel2Veh Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.2.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.2.2 Data Collection and Processing . . . . . . . . . . . . . . . . . . . . 50 5.2.3 Enhancement for Vehicle Flow Counting . . . . . . . . . . . . . . . 53 5.2.4 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3 Spatio-Temporal Fusion Framework . . . . . . . . . . . . . . . . . . 55 5.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.4.1 Experimental Settings and Baselines . . . . . . . . . . . . . . . . . 59 5.4.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 60 5.5 Applications for Future Expansion . . . . . . . . . . . . . . . . . . . 62 5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Chapter 6 Cross-Modal Prediction for Traffic Directionality 63 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 6.2 TeltoMob dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 6.2.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 6.2.2 Data Collection and Processing . . . . . . . . . . . . . . . . . . . . 66 6.2.3 Data Privacy Protection . . . . . . . . . . . . . . . . . . . . . . . . 67 6.2.4 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6.3.1 Task Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6.3.2 Framework Overview . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.3.3 Stage 1 of Framework . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.3.4 Stage 2 of Framework . . . . . . . . . . . . . . . . . . . . . . . . . 74 6.3.4.1 Transformation Step . . . . . . . . . . . . . . . . . . . 74 6.3.4.2 Enhancement Step . . . . . . . . . . . . . . . . . . . . 75 6.3.4.3 Prediction Step . . . . . . . . . . . . . . . . . . . . . . 77 6.3.4.4 Framework Training . . . . . . . . . . . . . . . . . . . 78 6.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.4.2 Prediction Performance . . . . . . . . . . . . . . . . . . . . . . . . 80 6.4.3 Ablation Study of Our Framework . . . . . . . . . . . . . . . . . . 81 6.4.4 Applications and Impact on Transportation . . . . . . . . . . . . . . 83 6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Chapter 7 Conclusion 87 References 89 Appendix — Publications 103 Appendix — Open Source Datasets 105 | - |
| dc.language.iso | en | - |
| dc.title | 串連電信與交通:推進圖神經網路於時空推理與多模態融合及預測 | zh_TW |
| dc.title | Bridging Telecom and Transportation:Advancing Graph Neural Networks for Spatio-Temporal Reasoning, Multimodal Fusion and Prediction | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 鄭卜壬;林守德;林忠緯;王鈺強;逄愛君;葉梅珍;王蒞君 | zh_TW |
| dc.contributor.oralexamcommittee | Pu-Jen Cheng;Shou-De Lin;Chung-Wei Lin;Yu-Chiang Wang;Ai-Chun Pang;Mei-Chen Yeh;Li-Chun Wang | en |
| dc.subject.keyword | 電信數據應用,圖神經網路,時空推理,多模態融合與對齊,跨模態預測, | zh_TW |
| dc.subject.keyword | Telecom Data Applications,Graph Neural Networks,Spatio-Temporal Reasoning,Multimodal Fusion and Alignment,Cross-Modal Prediction, | en |
| dc.relation.page | 105 | - |
| dc.identifier.doi | 10.6342/NTU202402338 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2024-07-27 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | - |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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