DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Yoon, Yoonjin | - |
dc.contributor.advisor | 윤윤진 | - |
dc.contributor.author | Shin, Yuyol | - |
dc.date.accessioned | 2023-06-21T19:32:52Z | - |
dc.date.available | 2023-06-21T19:32:52Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1007735&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/307776 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 건설및환경공학과, 2022.8,[v, 111 p. :] | - |
dc.description.abstract | Traffic forecasting has garnered research interest as a crucial technical component of adaptive traffic management over the last four decades. Since 2014, deep learning models have been actively proposed in this field of research, and graph neural networks have recently taken the major portion. Most studies, however, focus only on the accuracy of the models, and the robustness on various transportation networks and geospatial complexities is often neglected. To this end, this dissertation focuses on ensuring both the accuracy and robustness of traffic forecasting models based on graph neural networks. Techniques to effectively incorporate static structural characteristics depending on the geospatial complexity, and to reflect dynamic time-varying spatial correlation through online traffic data are studied. Also, a comprehensive assessment has been conducted to evaluate robustness and generalization capability of traffic forecasting models. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Deep learning▼aGraph neural networks▼aTraffic forecasting▼aBig data▼aTransportation networks | - |
dc.subject | 심층학습▼a그래프인공신경망▼a교통 예측▼a빅데이터▼a교통 네트워크 | - |
dc.title | Spatial-temporal graph neural networks for traffic forecasting across multiple study sites | - |
dc.title.alternative | 시공간적 그래프 인공 신경망을 활용한 교통예측 연구 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :건설및환경공학과, | - |
dc.contributor.alternativeauthor | 신유열 | - |
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