{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T14:27:48Z","timestamp":1782397668702,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 110-2636-E-006 -011, MOST 109-2636-E-006 -025, MOST 108-2636-E-006 -013"],"award-info":[{"award-number":["MOST 110-2636-E-006 -011, MOST 109-2636-E-006 -025, MOST 108-2636-E-006 -013"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,11,2]]},"DOI":"10.1145\/3474717.3484259","type":"proceedings-article","created":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T22:59:44Z","timestamp":1636066784000},"page":"554-563","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Dual-Attention Multi-Scale Graph Convolutional Networks for Highway Accident Delay Time Prediction"],"prefix":"10.1145","author":[{"given":"I-Ying","family":"Wu","sequence":"first","affiliation":[{"name":"Institute of Computer and Communication Engineering, National Cheng Kung University, Tainan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fandel","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Computer and Communication Engineering, National Cheng Kung University, Tainan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsun-Ping","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,11,4]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"86th Annual Transportation Research Conf., Transportation Research Board, Washington, DC (2006)","author":"Baykal-Gursoy M.","year":"2006","unstructured":"Baykal-Gursoy , M. , Xiao , W. , Duan , Z. and Ozbay , K . Delay estimation for traffic flow interrupted by incidents . In 86th Annual Transportation Research Conf., Transportation Research Board, Washington, DC (2006) 2006 . Baykal-Gursoy, M., Xiao, W., Duan, Z. and Ozbay, K. Delay estimation for traffic flow interrupted by incidents. In 86th Annual Transportation Research Conf., Transportation Research Board, Washington, DC (2006)2006."},{"key":"e_1_3_2_1_2_1","volume-title":"Random forests. Machine learning, 45, 1","author":"Breiman L.","year":"2001","unstructured":"Breiman , L. Random forests. Machine learning, 45, 1 ( 2001 ), 5--32. Breiman, L. Random forests. Machine learning, 45, 1 (2001), 5--32."},{"key":"e_1_3_2_1_3_1","volume-title":"Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203","author":"Bruna J.","year":"2013","unstructured":"Bruna , J. , Zaremba , W. , Szlam , A. and LeCun , Y. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 ( 2013 ). Bruna, J., Zaremba, W., Szlam, A. and LeCun, Y. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_5_1","volume-title":"Genetic algorithm approach for transit route planning and design. Journal of transportation engineering, 127, 3","author":"Chien S.","year":"2001","unstructured":"Chien , S. , Yang , Z. and Hou , E . Genetic algorithm approach for transit route planning and design. Journal of transportation engineering, 127, 3 ( 2001 ), 200--207. Chien, S., Yang, Z. and Hou, E. Genetic algorithm approach for transit route planning and design. Journal of transportation engineering, 127, 3 (2001), 200--207."},{"key":"e_1_3_2_1_6_1","volume-title":"Convolutional neural networks on graphs with fast localized spectral filtering. arXiv preprint arXiv:1606.09375","author":"Defferrard M.","year":"2016","unstructured":"Defferrard , M. , Bresson , X. and Vandergheynst , P . Convolutional neural networks on graphs with fast localized spectral filtering. arXiv preprint arXiv:1606.09375 ( 2016 ). Defferrard, M., Bresson, X. and Vandergheynst, P. Convolutional neural networks on graphs with fast localized spectral filtering. arXiv preprint arXiv:1606.09375 (2016)."},{"key":"e_1_3_2_1_7_1","first-page":"4","volume":"10","author":"Ge L.","year":"2020","unstructured":"Ge , L. , Li , S. , Wang , Y. , Chang , F. and Wu , K . Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction. Applied Sciences , 10 , 4 ( 2020 ), 1509. Ge, L., Li, S., Wang, Y., Chang, F. and Wu, K. Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction. Applied Sciences, 10, 4 (2020), 1509.","journal-title":"Applied Sciences"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_9_1","volume-title":"Entity embeddings of categorical variables. arXiv preprint arXiv:1604.06737","author":"Guo C.","year":"2016","unstructured":"Guo , C. and Berkhahn , F . Entity embeddings of categorical variables. arXiv preprint arXiv:1604.06737 ( 2016 ). Guo, C. and Berkhahn, F. Entity embeddings of categorical variables. arXiv preprint arXiv:1604.06737 (2016)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_1_11_1","volume-title":"Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584","author":"Hamilton W. L.","year":"2017","unstructured":"Hamilton , W. L. , Ying , R. and Leskovec , J . Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584 ( 2017 ). Hamilton, W. L., Ying, R. and Leskovec, J. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584 (2017)."},{"key":"e_1_3_2_1_12_1","unstructured":"Huang R. Huang C. Liu Y. Dai G. and Kong W. LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks (  Huang R. Huang C. Liu Y. Dai G. and Kong W. LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks ("},{"key":"e_1_3_2_1_13_1","volume-title":"International conference on machine learning (2015","author":"Ioffe S.","year":"2015","unstructured":"Ioffe , S. and Szegedy , C . Batch normalization: Accelerating deep network training by reducing internal covariate shift . In International conference on machine learning (2015 ). PMLR, 2015 . Ioffe, S. and Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (2015). PMLR, 2015."},{"key":"e_1_3_2_1_14_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf T. N.","year":"2016","unstructured":"Kipf , T. N. and Welling , M . Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 ( 2016 ). Kipf, T. N. and Welling, M. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_15_1","volume-title":"Deep learning. nature, 521, 7553","author":"LeCun Y.","year":"2015","unstructured":"LeCun , Y. , Bengio , Y. and Hinton , G . Deep learning. nature, 521, 7553 ( 2015 ), 436--444. LeCun, Y., Bengio, Y. and Hinton, G. Deep learning. nature, 521, 7553 (2015), 436--444."},{"key":"e_1_3_2_1_16_1","volume-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926","author":"Li Y.","year":"2017","unstructured":"Li , Y. , Yu , R. , Shahabi , C. and Liu , Y . Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 ( 2017 ). Li, Y., Yu, R., Shahabi, C. and Liu, Y. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330906"},{"key":"e_1_3_2_1_18_1","first-page":"18","article-title":"Traffic speed prediction: an attention-based method","volume":"19","author":"Liu D.","year":"2019","unstructured":"Liu , D. , Tang , L. , Shen , G. and Han , X . Traffic speed prediction: an attention-based method . Sensors , 19 , 18 ( 2019 ), 3836. Liu, D., Tang, L., Shen, G. and Han, X. Traffic speed prediction: an attention-based method. Sensors, 19, 18 (2019), 3836.","journal-title":"Sensors"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/atr.1216"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411894"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Lv Z. Xu J. Zheng K. Yin H. Zhao P. and Zhou X. Lc-rnn: A deep learning model for traffic speed prediction. In IJCAI (2018)2018.  Lv Z. Xu J. Zheng K. Yin H. Zhao P. and Zhou X. Lc-rnn: A deep learning model for traffic speed prediction. In IJCAI (2018)2018.","DOI":"10.24963\/ijcai.2018\/482"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207237208709519"},{"key":"e_1_3_2_1_23_1","volume-title":"Real-Time Traffic Congestion Prediction","author":"Mangharam R.","year":"2008","unstructured":"Mangharam , R. , Lee , I. and Sokolsky , O . Real-Time Traffic Congestion Prediction ( 2008 ). Mangharam, R., Lee, I. and Sokolsky, O. Real-Time Traffic Congestion Prediction (2008)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1515\/hjic-2016-0008"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1051\/matecconf\/201821803021"},{"key":"e_1_3_2_1_26_1","volume-title":"Stand-alone self-attention in vision models. arXiv preprint arXiv:1906.05909","author":"Ramachandran P.","year":"2019","unstructured":"Ramachandran , P. , Parmar , N. , Vaswani , A. , Bello , I. , Levskaya , A. and Shlens , J . Stand-alone self-attention in vision models. arXiv preprint arXiv:1906.05909 ( 2019 ). Ramachandran, P., Parmar, N., Vaswani, A., Bello, I., Levskaya, A. and Shlens, J. Stand-alone self-attention in vision models. arXiv preprint arXiv:1906.05909 (2019)."},{"key":"e_1_3_2_1_27_1","volume-title":"Feature importance estimation with self-attention networks. arXiv preprint arXiv:2002.04464","author":"\u0160krlj B.","year":"2020","unstructured":"\u0160krlj , B. , D\u017eeroski , S. , Lavra\u010d , N. and Petkovi\u010d , M . Feature importance estimation with self-attention networks. arXiv preprint arXiv:2002.04464 ( 2020 ). \u0160krlj, B., D\u017eeroski, S., Lavra\u010d, N. and Petkovi\u010d, M. Feature importance estimation with self-attention networks. arXiv preprint arXiv:2002.04464 (2020)."},{"key":"e_1_3_2_1_28_1","first-page":"163","article-title":"The importance of automotive industry in shaping habitants mobility in future cities","volume":"71","author":"Szmelter A","year":"2017","unstructured":"Szmelter , A . The importance of automotive industry in shaping habitants mobility in future cities . Transport Economics and Logistics , 71 ( 2017 ), 163 -- 178 . Szmelter, A. The importance of automotive industry in shaping habitants mobility in future cities. Transport Economics and Logistics, 71 (2017), 163--178.","journal-title":"Transport Economics and Logistics"},{"key":"e_1_3_2_1_29_1","volume-title":"The 22nd International Conference on Artificial Intelligence and Statistics (2019","author":"Vashishth S.","year":"2019","unstructured":"Vashishth , S. , Yadav , P. , Bhandari , M. and Talukdar , P . Confidence-based graph convolutional networks for semi-supervised learning . In The 22nd International Conference on Artificial Intelligence and Statistics (2019 ). PMLR, 2019 . Vashishth, S., Yadav, P., Bhandari, M. and Talukdar, P. Confidence-based graph convolutional networks for semi-supervised learning. In The 22nd International Conference on Artificial Intelligence and Statistics (2019). PMLR, 2019."},{"key":"e_1_3_2_1_30_1","volume-title":"Attention is all you need. arXiv preprint arXiv:1706.03762","author":"Vaswani A.","year":"2017","unstructured":"Vaswani , A. , Shazeer , N. , Parmar , N. , Uszkoreit , J. , Jones , L. , Gomez , A. N. , Kaiser , L. and Polosukhin , I . Attention is all you need. arXiv preprint arXiv:1706.03762 ( 2017 ). Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. and Polosukhin, I. Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)."},{"key":"e_1_3_2_1_31_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Veli\u010dkovi\u0107 P.","year":"2017","unstructured":"Veli\u010dkovi\u0107 , P. , Cucurull , G. , Casanova , A. , Romero , A. , Lio , P. and Bengio , Y . Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P. and Bengio, Y. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_32_1","volume-title":"Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121","author":"Wu Z.","year":"2019","unstructured":"Wu , Z. , Pan , S. , Long , G. , Jiang , J. and Zhang , C . Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 ( 2019 ). Wu, Z., Pan, S., Long, G., Jiang, J. and Zhang, C. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411873"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42452-020-3125-1"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phpro.2012.02.231"},{"key":"e_1_3_2_1_36_1","volume-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875","author":"Yu B.","year":"2017","unstructured":"Yu , B. , Yin , H. and Zhu , Z . Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 ( 2017 ). Yu, B., Yin, H. and Zhu, Z. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219922"},{"key":"e_1_3_2_1_38_1","volume-title":"Traffic congestion prediction based on Estimated Time of Arrival. PloS one, 15, 12","author":"Zafar N.","year":"2020","unstructured":"Zafar , N. and Ul Haq , I. Traffic congestion prediction based on Estimated Time of Arrival. PloS one, 15, 12 ( 2020 ), e0238200. Zafar, N. and Ul Haq, I. Traffic congestion prediction based on Estimated Time of Arrival. PloS one, 15, 12 (2020), e0238200."},{"key":"e_1_3_2_1_39_1","first-page":"2020","article-title":"Traffic accident prediction based on LSTM-GBRT model","author":"Zhang Z.","year":"2020","unstructured":"Zhang , Z. , Yang , W. and Wushour , S . Traffic accident prediction based on LSTM-GBRT model . Journal of Control Science and Engineering , 2020 ( 2020 ). Zhang, Z., Yang, W. and Wushour, S. Traffic accident prediction based on LSTM-GBRT model. Journal of Control Science and Engineering, 2020 (2020).","journal-title":"Journal of Control Science and Engineering"},{"key":"e_1_3_2_1_40_1","volume-title":"Research on Traffic Accident Prediction Model Based on Convolutional Neural Networks in VANET. In 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) (2019","author":"Zhao H.","year":"2019","unstructured":"Zhao , H. , Cheng , H. , Mao , T. and He , C . Research on Traffic Accident Prediction Model Based on Convolutional Neural Networks in VANET. In 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) (2019 ). IEEE, 2019 . Zhao, H., Cheng, H., Mao, T. and He, C. Research on Traffic Accident Prediction Model Based on Convolutional Neural Networks in VANET. In 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) (2019). IEEE, 2019."},{"key":"e_1_3_2_1_41_1","first-page":"2018","article-title":"Highway travel time prediction using sparse tensor completion tactics and-nearest neighbor pattern matching method","author":"Zhao J.","year":"2018","unstructured":"Zhao , J. , Gao , Y. , Tang , J. , Zhu , L. and Ma , J . Highway travel time prediction using sparse tensor completion tactics and-nearest neighbor pattern matching method . Journal of Advanced Transportation , 2018 ( 2018 ). Zhao, J., Gao, Y., Tang, J., Zhu, L. and Ma, J. Highway travel time prediction using sparse tensor completion tactics and-nearest neighbor pattern matching method. Journal of Advanced Transportation, 2018 (2018).","journal-title":"Journal of Advanced Transportation"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"}],"event":{"name":"SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems","location":"Beijing China","acronym":"SIGSPATIAL '21","sponsor":["SIGSPATIAL ACM Special Interest Group on Spatial Information"]},"container-title":["Proceedings of the 29th International Conference on Advances in Geographic Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474717.3484259","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3474717.3484259","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:11:46Z","timestamp":1750191106000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474717.3484259"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,2]]},"references-count":42,"alternative-id":["10.1145\/3474717.3484259","10.1145\/3474717"],"URL":"https:\/\/doi.org\/10.1145\/3474717.3484259","relation":{},"subject":[],"published":{"date-parts":[[2021,11,2]]},"assertion":[{"value":"2021-11-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}