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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曹承礎 | zh_TW |
| dc.contributor.advisor | Seng-Cho Chou | en |
| dc.contributor.author | 王佩晨 | zh_TW |
| dc.contributor.author | Pei-Chen Wang | en |
| dc.date.accessioned | 2023-10-03T17:40:11Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-10-03 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-06-07 | - |
| dc.identifier.citation | BAI, L., Yao, L., Li, C., Wang, X., and Wang, C. (2020). Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. In Larochelle, H., Ranzato, M., Hadsell, R.,
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Adaptive Graph Convolutional Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Li, Y., Yu, R., Shahabi, C., and Liu, Y. (2017). Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. Li, Z., Huang, C., Xia, L., Xu, Y., and Pei, J. (2022). Spatial Temporal Hypergraph Self-Supervised Learning for Crime Prediction. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE. Lu, B., Gan, X., Jin, H., Fu, L., and Zhang, H. (2020a). Spatiotemporal Adap tive Gated Graph Convolution Network for Urban Traffic Flow Forecasting. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM ’20, page 1025–1034, New York, NY, USA. Association for Computing Machinery. Lu, B., Gan, X., Jin, H., Fu, L., and Zhang, H. (2020b). Spatiotemporal adaptive gated graph convolution network for urban traffic flow forecasting. 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Xia, L., Huang, C., Xu, Y., Dai, P., Bo, L., Zhang, X., and Chen, T. (2021). Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pages 1631–1637. International Joint Conferences on Artificial Intelligence Organization. Main Track. Yu, B., Yin, H., and Zhu, Z. (2017). Spatio-temporal Graph Convolutional Neural Net work: A Deep Learning Framework for Traffic Forecasting. CoRR, abs/1709.04875. Zhang, J., Shi, X., Xie, J., Ma, H., King, I., and Yeung, D.-Y. (2018). GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. Zhang, K., Li, T., Shen, S., Liu, B., Chen, J., and Liu, Q. (2020). Adaptive Graph Convolutional Network With Attention Graph Clustering for Co-Saliency Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90798 | - |
| dc.description.abstract | 犯罪預測在近幾年逐漸成為一個重要的議題，其有助於協助政府維護社會安全。而隨著深度學習的成熟和圖神經網絡的興起，越來越多學者投入這個領域的研究，透過各種方法來增加預測的準確率。然而，目前的研究都僅限於陸地上的犯罪預測，忽略的預防海上犯罪的重要性。海上犯罪影響的層面涉及社會治安及環境保護，且擁有的人力資源更稀少、範圍更廣，因此海上犯罪預測任務成為一大挑戰。
在本研究中，我們提出了一個名為ST-AAGAT的新時空圖網絡模型架構。此模型致力於透過圖神經網絡找到空間的相關性，再藉由Autoformer架構尋找犯罪發生的週期性，並結合捕捉到的時間和空間特徵進行時間序列的預測。我們將此模型在台灣海巡署的真實犯罪資料集上進行多個不同面向的實驗，結果顯示 ST-AAGAT相較於其他模型有更好的表現。透過這項研究，我們希望能夠協助海巡署更有效地進行人力派遣，同時實現海上犯罪的預防和提高環境保護的意識。 | zh_TW |
| dc.description.abstract | Crime prediction has become an important issue in recent years as it helps governments maintain social security. With the maturity of deep learning and the rise of graph neural networks, more researchers have been studying this field, employing various methods to enhance prediction accuracy. However, current research is limited to crime prediction on land, neglecting the significance of preventing maritime crimes. Due to the limited human resources and a broader scope, maritime crime prediction has become challenging.
In this study, we propose a novel spatial-temporal graph neural network model called ST-AAGAT. This model aims to identify spatial correlations through graph neural networks and leverage the Autoformer architecture to find the periodicity of crime. After that, we combine temporal and spatial patterns to predict the crime occurrence probability from time series data. We conducted experiments on maritime crime datasets with various aspects, and the results demonstrated that ST-AAGAT outperformed other models. Through this research, we hope to assist the coast guard administration in deploying resources more efficiently, preventing maritime crimes, and raising awareness for environmental protection. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T17:40:11Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-10-03T17:40:11Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgements i
摘要 ii Abstract iii Contents iv List of Figures vi List of Tables vii Chapter 1 Introduction. . .1 1.1 Background and Motivation . . . 1 1.2 Objectives . . . 3 1.3 Paper Organization . . .5 Chapter 2 Related Works. . . 6 2.1 Time Series Forecasting . . . 6 2.2 Graph Neural Networks . . .7 2.3 Spatial-Temporal Graph Neural Networks . . . 8 2.4 Crime Prediction . . . . 10 Chapter 3 Methodology. . .12 3.1 Task Definition . . . 12 3.2 Model Overview . . . 13 3.3 Graph Generation Module . . . 14 3.3.1 Spatial Graph . . . 14 3.3.2 Dynamic Time Warping Graph . . . 15 3.3.3 Adaptive Graph . . . 15 3.4 GAT Layer . . . 16 3.5 NAPL Layer . . . 17 3.6 Residual Connection and Graph Concatenation . . . 18 3.7 Embedding Layer . . . 19 3.8 Autoformer Module . . . 19 3.9 Gated Fusion Module . . . 22 Chapter 4 Experiments . . . 23 4.1 Dataset . . . 23 4.2 Baseline Models . . . 26 4.3 Experimental Results . . . 28 4.3.1 The Impact of Varying Number of Nodes . . .28 4.3.2 The Impact of Different Time Sequence Lengths . . . 30 4.3.3 Results of Different Prediction Lengths . . . 33 4.3.4 The Impact of Temporal on Classification Performance . . . 37 4.4 Ablation Study . . . 38 Chapter 5 Conclusions. . . 40 References. . . 42 | - |
| dc.language.iso | en | - |
| dc.subject | 時間序列預測 | zh_TW |
| dc.subject | Autoformer | zh_TW |
| dc.subject | 海上犯罪預測 | zh_TW |
| dc.subject | 時空圖神經網絡 | zh_TW |
| dc.subject | spatial-temporal GNN | en |
| dc.subject | Maritime crime prediction | en |
| dc.subject | Autoformer | en |
| dc.subject | time series model | en |
| dc.title | 基於時空圖神經網絡之海上犯罪預測 | zh_TW |
| dc.title | Maritime Crime Prediction with Spatial-Temporal Graph Neural Network | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 陳建錦;林俊叡 | zh_TW |
| dc.contributor.oralexamcommittee | Chien-Chin Chen;June-Ray Lin | en |
| dc.subject.keyword | 海上犯罪預測,時空圖神經網絡,Autoformer,時間序列預測, | zh_TW |
| dc.subject.keyword | Maritime crime prediction,spatial-temporal GNN,Autoformer,time series model, | en |
| dc.relation.page | 45 | - |
| dc.identifier.doi | 10.6342/NTU202300954 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-06-08 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 資訊管理學系 | - |
| 顯示於系所單位： | 資訊管理學系 | |
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