DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hwang, Jiyoung | ko |
dc.contributor.author | Noh, Byeongjoon | ko |
dc.contributor.author | Jin, Zhixiong | ko |
dc.contributor.author | Yeo, Hwasoo | ko |
dc.date.accessioned | 2023-05-10T11:00:14Z | - |
dc.date.available | 2023-05-10T11:00:14Z | - |
dc.date.created | 2023-05-03 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.citation | IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pp.1498 - 1503 | - |
dc.identifier.issn | 2153-0009 | - |
dc.identifier.uri | http://hdl.handle.net/10203/306682 | - |
dc.description.abstract | Traffic speed prediction is essential for efficient traffic operation and management by distributing demand concentration in time and space. To make an accurate prediction, it is required to consider spatio-temporal characteristics of the traffic evolution. Recently, deep learning-based approaches, especially Graph Neural Network (GNN) has been widely adopted to reflect the stated characteristics. However, existing GNN models mainly used for short-term prediction, whereas long-term traffic prediction is more useful by enabling earlier and efficient decisions of traffic management as well as individual travels. In this study, we propose Asymmetric Long-Term Graph Multi-Attention Network (ALT-GMAN) algorithm, an extension of the GMAN. ALT-GMAN can predict short and long-term traffic speed by considering asymmetric characteristics of forward and backward waves observed in real roadways. ALT-GMAN is tested with six months highway data of PeMS-Bay area, and MAPE for 3-hours and 6-hours prediction is evaluated as 5.53% and 6.05%, respectively. ALTGMAN outperforms the existing models in short-term speed prediction, and provides a robust performance in long-term prediction problems, too. | - |
dc.language | English | - |
dc.publisher | IEEE | - |
dc.title | Asymmetric Long-Term Graph Multi-Attention Network for Traffic Speed Prediction | - |
dc.type | Conference | - |
dc.identifier.wosid | 000934720601077 | - |
dc.identifier.scopusid | 2-s2.0-85141885283 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 1498 | - |
dc.citation.endingpage | 1503 | - |
dc.citation.publicationname | IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) | - |
dc.identifier.conferencecountry | CC | - |
dc.identifier.conferencelocation | Macau | - |
dc.identifier.doi | 10.1109/ITSC55140.2022.9922130 | - |
dc.contributor.localauthor | Yeo, Hwasoo | - |
dc.contributor.nonIdAuthor | Hwang, Jiyoung | - |
dc.contributor.nonIdAuthor | Jin, Zhixiong | - |
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