{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T07:00:46Z","timestamp":1673506846354},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2153311"]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539236","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling Network-level Traffic Flow Transitions on Sparse Data"],"prefix":"10.1145","author":[{"given":"Xiaoliang","family":"Lei","sequence":"first","affiliation":[{"name":"Xi'an Jiaotong University, Xi'an, China"}]},{"given":"Hao","family":"Mei","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technology, Newark, NJ, USA"}]},{"given":"Bin","family":"Shi","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, Xi'an, China"}]},{"given":"Hua","family":"Wei","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technology, Newark, NJ, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Store-and-forward based methods for the signal control problem in large-scale congested urban road networks. TR-C 17, 2","author":"Aboudolas Konstantinos","year":"2009","unstructured":"Konstantinos Aboudolas , Markos Papageorgiou , and Elias Kosmatopoulos . 2009. Store-and-forward based methods for the signal control problem in large-scale congested urban road networks. TR-C 17, 2 ( 2009 ). Konstantinos Aboudolas, Markos Papageorgiou, and Elias Kosmatopoulos. 2009. Store-and-forward based methods for the signal control problem in large-scale congested urban road networks. TR-C 17, 2 (2009)."},{"key":"e_1_3_2_2_2_1","first-page":"251","article-title":"A study of K-nearest neighbour as an imputation method","volume":"87","author":"Batista Gustavo EAPA","year":"2002","unstructured":"Gustavo EAPA Batista , Maria Carolina Monard , 2002 . A study of K-nearest neighbour as an imputation method . His 87 , 251 -- 260 (2002). Gustavo EAPA Batista, Maria Carolina Monard, et al. 2002. A study of K-nearest neighbour as an imputation method. His 87, 251--260 (2002).","journal-title":"His"},{"key":"e_1_3_2_2_3_1","volume-title":"A compositional stochastic model for real time freeway traffic simulation. TR-C","author":"Boel Ren\u00e9","year":"2006","unstructured":"Ren\u00e9 Boel and Lyudmila Mihaylova . 2006. A compositional stochastic model for real time freeway traffic simulation. TR-C ( 2006 ). Ren\u00e9 Boel and Lyudmila Mihaylova. 2006. A compositional stochastic model for real time freeway traffic simulation. TR-C (2006)."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467430"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403386"},{"key":"e_1_3_2_2_6_1","unstructured":"Pedro J Garc\u00eda-Laencina Jos\u00e9-Luis Sancho-G\u00f3mez etal 2010. Pattern classification with missing data: a review. Neural Computing and Applications (2010). Pedro J Garc\u00eda-Laencina Jos\u00e9-Luis Sancho-G\u00f3mez et al. 2010. Pattern classification with missing data: a review. Neural Computing and Applications (2010)."},{"key":"e_1_3_2_2_7_1","unstructured":"Shengnan Guo Youfang Lin etal 2021. Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting. TKDE (2021). Shengnan Guo Youfang Lin et al. 2021. Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting. TKDE (2021)."},{"key":"e_1_3_2_2_8_1","volume-title":"Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. AAAI'19","author":"Guo Shengnan","year":"2019","unstructured":"Shengnan Guo , Youfang Lin , Ning Feng , Chao Song , and Huaiyu Wan . 2019 . Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. AAAI'19 (2019). Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. AAAI'19 (2019)."},{"key":"e_1_3_2_2_9_1","volume-title":"Signalized intersection delay models--a primer for the uninitiated. TR-B","author":"Hurdle VF","year":"1984","unstructured":"VF Hurdle . 1984. Signalized intersection delay models--a primer for the uninitiated. TR-B ( 1984 ). VF Hurdle. 1984. Signalized intersection delay models--a primer for the uninitiated. TR-B (1984)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Kasthuri Jayarajah Andrew Tan and Archan Misra. 2018. Understanding the interdependency of land use and mobility for urban planning. In UbiComp'18. Kasthuri Jayarajah Andrew Tan and Archan Misra. 2018. Understanding the interdependency of land use and mobility for urban planning. In UbiComp'18.","DOI":"10.1145\/3267305.3274163"},{"key":"e_1_3_2_2_11_1","volume-title":"STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting. CIKM'20","author":"Kim Kihwan","year":"2020","unstructured":"Kihwan Kim , Seungmin Jin , 2020 . STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting. CIKM'20 (2020). Kihwan Kim, Seungmin Jin, et al. 2020. STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting. CIKM'20 (2020)."},{"key":"e_1_3_2_2_12_1","volume-title":"Matrix factorization techniques for recommender systems. Computer 42, 8","author":"Koren Yehuda","year":"2009","unstructured":"Yehuda Koren , Robert Bell , and Chris Volinsky . 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 ( 2009 ). Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009)."},{"key":"e_1_3_2_2_13_1","volume-title":"Traffic flow modeling of large-scale motorway networks using the macroscopic modeling tool METANET. TITS","author":"Kotsialos Apostolos","year":"2002","unstructured":"Apostolos Kotsialos , Markos Papageorgiou , Christina Diakaki , Yannis Pavlis , and Frans Middelham . 2002. Traffic flow modeling of large-scale motorway networks using the macroscopic modeling tool METANET. TITS ( 2002 ). Apostolos Kotsialos, Markos Papageorgiou, Christina Diakaki, Yannis Pavlis, and Frans Middelham. 2002. Traffic flow modeling of large-scale motorway networks using the macroscopic modeling tool METANET. TITS (2002)."},{"key":"e_1_3_2_2_14_1","volume-title":"Misgan: Learning from incomplete data with generative adversarial networks. arXiv:1902.09599","author":"Cheng-Xian Li Steven","year":"2019","unstructured":"Steven Cheng-Xian Li , Bo Jiang , 2019 . Misgan: Learning from incomplete data with generative adversarial networks. arXiv:1902.09599 (2019). Steven Cheng-Xian Li, Bo Jiang, et al. 2019. Misgan: Learning from incomplete data with generative adversarial networks. arXiv:1902.09599 (2019)."},{"key":"e_1_3_2_2_15_1","unstructured":"Yaguang Li Rose Yu etal 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv:1707.01926 (2017). Yaguang Li Rose Yu et al. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv:1707.01926 (2017)."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219993"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.9971"},{"key":"e_1_3_2_2_18_1","unstructured":"Yonghong Luo Xiangrui Cai Ying Zhang Jun Xu etal 2018. Multivariate time series imputation with generative adversarial networks. In NeurIPS'18. Yonghong Luo Xiangrui Cai Ying Zhang Jun Xu et al. 2018. Multivariate time series imputation with generative adversarial networks. In NeurIPS'18."},{"key":"e_1_3_2_2_19_1","volume-title":"Spectral regularization algorithms for learning large incomplete matrices. JMLR","author":"Mazumder Rahul","year":"2010","unstructured":"Rahul Mazumder , Trevor Hastie , and Robert Tibshirani . 2010. Spectral regularization algorithms for learning large incomplete matrices. JMLR ( 2010 ). Rahul Mazumder, Trevor Hastie, and Robert Tibshirani. 2010. Spectral regularization algorithms for learning large incomplete matrices. JMLR (2010)."},{"key":"e_1_3_2_2_20_1","volume-title":"Short-term traveltime prediction on highway: A review on model-based approach. KSCE Journal of Civil Engineering","author":"Oh Simon","year":"2018","unstructured":"Simon Oh , Young-Ji Byon , Kitae Jang , and Hwasoo Yeo . 2018. Short-term traveltime prediction on highway: A review on model-based approach. KSCE Journal of Civil Engineering ( 2018 ). Simon Oh, Young-Ji Byon, Kitae Jang, and Hwasoo Yeo. 2018. Short-term traveltime prediction on highway: A review on model-based approach. KSCE Journal of Civil Engineering (2018)."},{"key":"e_1_3_2_2_21_1","unstructured":"Huiling Qin Xianyuan Zhan Yuanxun Li etal 2021. Network-Wide Traffic States Imputation Using Self-interested Coalitional Learning. (2021). Huiling Qin Xianyuan Zhan Yuanxun Li et al. 2021. Network-Wide Traffic States Imputation Using Self-interested Coalitional Learning. (2021)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Xianfeng Tang Boqing Gong Yanwei Yu etal 2019. Joint modeling of dense and incomplete trajectories for citywide traffic volume inference. In TheWebConf'19. Xianfeng Tang Boqing Gong Yanwei Yu et al. 2019. Joint modeling of dense and incomplete trajectories for citywide traffic volume inference. In TheWebConf'19.","DOI":"10.1145\/3308558.3313621"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2013.08.014"},{"key":"e_1_3_2_2_25_1","unstructured":"Hongjian Wang Xianfeng Tang etal 2019. A simple baseline for travel time estimation using large-scale trip data. TIST (2019). Hongjian Wang Xianfeng Tang et al. 2019. A simple baseline for travel time estimation using large-scale trip data. TIST (2019)."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3097985"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Xiaoyang Wang Yao Ma Yiqi Wang Wei Jin Xin Wang etal 2020. Traffic flow prediction via spatial temporal graph neural network. In TheWebConf'20. Xiaoyang Wang Yao Ma Yiqi Wang Wei Jin Xin Wang et al. 2020. Traffic flow prediction via spatial temporal graph neural network. In TheWebConf'20.","DOI":"10.1145\/3366423.3380186"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Yibing Wang Mingming Zhao etal 2022. Real-time joint traffic state and model parameter estimation on freeways with fixed sensors and connected vehicles: State-of-the-art overview methods and case studies. TR-C (2022). Yibing Wang Mingming Zhao et al. 2022. Real-time joint traffic state and model parameter estimation on freeways with fixed sensors and connected vehicles: State-of-the-art overview methods and case studies. TR-C (2022).","DOI":"10.1016\/j.trc.2021.103444"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330949"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220096"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460567"},{"key":"e_1_3_2_2_32_1","unstructured":"Zonghan Wu Shirui Pan Guodong Long etal 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv:1906.00121 (2019). Zonghan Wu Shirui Pan Guodong Long et al. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv:1906.00121 (2019)."},{"key":"e_1_3_2_2_33_1","volume-title":"CIKM'20","author":"Wu Zonghan","year":"2020","unstructured":"Zonghan Wu , Shirui Pan , Guodong Long , 2020 . Connecting the dots: Multivariate time series forecasting with graph neural networks . In CIKM'20 . Zonghan Wu, Shirui Pan, Guodong Long, et al. 2020. Connecting the dots: Multivariate time series forecasting with graph neural networks. In CIKM'20."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015668"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11836"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357822"},{"key":"e_1_3_2_2_37_1","volume-title":"ICML'18","author":"Yoon Jinsung","year":"2018","unstructured":"Jinsung Yoon , James Jordon , and Mihaela Schaar . 2018 . Gain: Missing data imputation using generative adversarial nets . In ICML'18 . Jinsung Yoon, James Jordon, and Mihaela Schaar. 2018. Gain: Missing data imputation using generative adversarial nets. In ICML'18."},{"key":"e_1_3_2_2_38_1","volume-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv:1709.04875","author":"Yu Bing","year":"2017","unstructured":"Bing Yu , Haoteng Yin , and Zhanxing Zhu . 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv:1709.04875 ( 2017 ). Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv:1709.04875 (2017)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.87"},{"key":"e_1_3_2_2_40_1","volume-title":"Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In TheWebConf'19.","author":"Zhang Huichu","year":"2019","unstructured":"Huichu Zhang , Siyuan Feng , 2019 . Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In TheWebConf'19. Huichu Zhang, Siyuan Feng, et al. 2019. Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In TheWebConf'19."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411941"},{"key":"e_1_3_2_2_43_1","volume-title":"Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network. AAAI'20","author":"Zhang Xiyue","year":"2020","unstructured":"Xiyue Zhang , Chao Huang , Yong Xu , 2020 . Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network. AAAI'20 (2020). Xiyue Zhang, Chao Huang, Yong Xu, et al. 2020. Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network. AAAI'20 (2020)."},{"key":"e_1_3_2_2_44_1","volume-title":"TITS'19","author":"Zhao Ling","year":"2019","unstructured":"Ling Zhao , Yujiao Song , 2019 . T-gcn: A temporal graph convolutional network for traffic prediction . TITS'19 (2019). Ling Zhao, Yujiao Song, et al. 2019. T-gcn: A temporal graph convolutional network for traffic prediction. TITS'19 (2019)."}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539236","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539236","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T05:58:59Z","timestamp":1673416739000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539236"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":44,"alternative-id":["10.1145\/3534678.3539236","10.1145\/3534678"],"URL":"http:\/\/dx.doi.org\/10.1145\/3534678.3539236","relation":{},"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}