{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T22:29:44Z","timestamp":1792621784604,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":31,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,8,23]]},"DOI":"10.1145\/3394486.3403386","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T19:04:00Z","timestamp":1597950240000},"page":"3337-3345","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":73,"title":["CompactETA"],"prefix":"10.1145","author":[{"given":"Kun","family":"Fu","sequence":"first","affiliation":[{"name":"AI Labs, Didi Chuxing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fanlin","family":"Meng","sequence":"additional","affiliation":[{"name":"AI Labs, Didi Chuxing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jieping","family":"Ye","sequence":"additional","affiliation":[{"name":"AI Labs, Didi Chuxing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"AI Labs, Didi Chuxing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2820783.2820836"},{"key":"e_1_3_2_1_2_1","unstructured":"Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2014. Spectral networks and locally connected networks on graphs. In ICLR.  Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2014. Spectral networks and locally connected networks on graphs. In ICLR."},{"key":"e_1_3_2_1_3_1","unstructured":"Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in neural information processing systems (NeurIPS). 3844--3852.  Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in neural information processing systems (NeurIPS). 3844--3852."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2016.7795686"},{"key":"e_1_3_2_1_5_1","volume-title":"Traffic Estimation And Prediction Based On Real Time Floating Car Data. In 2008 11th International IEEE Conference on Intelligent Transportation Systems.","author":"de Fabritiis C.","unstructured":"C. de Fabritiis , R. Ragona , and G. Valenti . 2008 . Traffic Estimation And Prediction Based On Real Time Floating Car Data. In 2008 11th International IEEE Conference on Intelligent Transportation Systems. C. de Fabritiis, R. Ragona, and G. Valenti. 2008. Traffic Estimation And Prediction Based On Real Time Floating Car Data. In 2008 11th International IEEE Conference on Intelligent Transportation Systems."},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 69--78","author":"Wang-Chien Lee Fu","year":"2019","unstructured":"Tao-yang Fu and Wang-Chien Lee . 2019 . DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation . In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 69--78 . Tao-yang Fu and Wang-Chien Lee. 2019. DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 69--78."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/234"},{"key":"e_1_3_2_1_8_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems.  Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_9_1","volume-title":"Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163","author":"Henaff Mikael","year":"2015","unstructured":"Mikael Henaff , Joan Bruna , and Yann LeCun . 2015. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163 ( 2015 ). Mikael Henaff, Joan Bruna, and Yann LeCun. 2015. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163 (2015)."},{"key":"e_1_3_2_1_10_1","volume-title":"Long short-term memory. Neural computation","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation , Vol. 9 , 8 ( 1997 ), 1735--1780. Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_1_11_1","volume-title":"Adam: A Method for Stochastic Optimization. In ICLR.","author":"Kingma Diederik","year":"2015","unstructured":"Diederik Kingma and Jimmy Ba . 2015 . Adam: A Method for Stochastic Optimization. In ICLR. Diederik Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR."},{"key":"e_1_3_2_1_12_1","unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR.  Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"e_1_3_2_1_13_1","volume-title":"Bus Travel Time Predictions Using Additive Models. In IEEE International Conference on Data Mining (ICDM '14)","author":"Korm\u00e1ksson M.","unstructured":"M. Korm\u00e1ksson , L. Barbosa , M. R. Vieira , and B. Zadrozny . 2014 . Bus Travel Time Predictions Using Additive Models. In IEEE International Conference on Data Mining (ICDM '14) . M. Korm\u00e1ksson, L. Barbosa, M. R. Vieira, and B. Zadrozny. 2014. Bus Travel Time Predictions Using Additive Models. In IEEE International Conference on Data Mining (ICDM '14)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2424321.2424357"},{"key":"e_1_3_2_1_16_1","volume-title":"Learning Travel Time Distributions with Deep Generative Model. In The World Wide Web Conference. ACM, 1017--1027","author":"Li Xiucheng","year":"2019","unstructured":"Xiucheng Li , Gao Cong , Aixin Sun , and Yun Cheng . 2019 . Learning Travel Time Distributions with Deep Generative Model. In The World Wide Web Conference. ACM, 1017--1027 . Xiucheng Li, Gao Cong, Aixin Sun, and Yun Cheng. 2019. Learning Travel Time Distributions with Deep Generative Model. In The World Wide Web Conference. ACM, 1017--1027."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220033"},{"key":"e_1_3_2_1_18_1","unstructured":"Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2018b. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In ICLR.  Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2018b. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In ICLR."},{"key":"e_1_3_2_1_19_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (NeurIPS). 3111--3119.  Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (NeurIPS). 3111--3119."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2019.8917465"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2008.915649"},{"key":"e_1_3_2_1_23_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems (NeurIPS). 5998--6008.  Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems (NeurIPS). 5998--6008."},{"key":"e_1_3_2_1_24_1","unstructured":"Petar Velivc kovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph attention networks. In ICLR.  Petar Velivc kovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph attention networks. In ICLR."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11877"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2996913.2996943"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623656"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219900"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1177\/1687814016678162"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2004.837813"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"}],"event":{"name":"KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event CA USA","acronym":"KDD '20","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 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403386","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403386","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:31:30Z","timestamp":1750181490000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403386"}},"subtitle":["A Fast Inference System for Travel Time Prediction"],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":31,"alternative-id":["10.1145\/3394486.3403386","10.1145\/3394486"],"URL":"https:\/\/doi.org\/10.1145\/3394486.3403386","relation":{},"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"2020-08-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}