{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T15:25:21Z","timestamp":1778599521084,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":30,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFC3303600"],"award-info":[{"award-number":["2022YFC3303600"]}]},{"name":"Innovation Research Team of Ministry of Education","award":["IRT_17R86"],"award-info":[{"award-number":["IRT_17R86"]}]},{"name":"Guangzhou Science and Technology Plan Guangzhou-HKUST(GZ) Joint Project","award":["2023A03J0144"],"award-info":[{"award-number":["2023A03J0144"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62137002, 62293553, 62102110"],"award-info":[{"award-number":["62137002, 62293553, 62102110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Program of Shaanxi Province","doi-asserted-by":"publisher","award":["2023-JC-YB-293"],"award-info":[{"award-number":["2023-JC-YB-293"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovative Research Group of the National Natural Science Foundation of China","award":["61721002"],"award-info":[{"award-number":["61721002"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3614730","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:26Z","timestamp":1697874326000},"page":"4794-4800","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["GBTTE: Graph Attention Network Based Bus Travel Time Estimation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3783-3797","authenticated-orcid":false,"given":"Yuecheng","family":"Rong","sequence":"first","affiliation":[{"name":"Xi'an Jiaotong University, Baidu Inc., Xi'an, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3347-6400","authenticated-orcid":false,"given":"Juntao","family":"Yao","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6004-0675","authenticated-orcid":false,"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8037-1394","authenticated-orcid":false,"given":"Yifan","family":"Fang","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9931-6482","authenticated-orcid":false,"given":"Wei","family":"Luo","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4271-1567","authenticated-orcid":false,"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou) & The Hong Kong University of Science and Technology, Guangzhou, HongKong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7432-3238","authenticated-orcid":false,"given":"Jie","family":"Ma","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3066-4094","authenticated-orcid":false,"given":"Zepeng","family":"Dan","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6389-3019","authenticated-orcid":false,"given":"Jinzhu","family":"Lin","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8703-0706","authenticated-orcid":false,"given":"Zhi","family":"Wu","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5729-6101","authenticated-orcid":false,"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0462-2126","authenticated-orcid":false,"given":"Chuanming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11205-020-02284-0"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Tianqi Chen Tong He Michael Benesty Vadim Khotilovich and Yuan Tang. 2015. xgboost: Extreme Gradient Boosting. (2015). Tianqi Chen Tong He Michael Benesty Vadim Khotilovich and Yuan Tang. 2015. xgboost: Extreme Gradient Boosting. (2015).","DOI":"10.32614\/CRAN.package.xgboost"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.12.114"},{"key":"e_1_3_2_1_4_1","volume-title":"ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps (KDD '20)","author":"Fang Xiaomin","unstructured":"Xiaomin Fang , Jizhou Huang , Fan Wang , Lingke Zeng , Haijin Liang , and Haifeng Wang . 2020. ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps (KDD '20) . Association for Computing Machinery , New York, NY, USA , 2697--2705. https:\/\/doi.org\/10.1145\/3394486.3403320 10.1145\/3394486.3403320 Xiaomin Fang, Jizhou Huang, Fan Wang, Lingke Zeng, Haijin Liang, and Haifeng Wang. 2020. ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps (KDD '20). Association for Computing Machinery, New York, NY, USA, 2697--2705. https:\/\/doi.org\/10.1145\/3394486.3403320"},{"key":"e_1_3_2_1_5_1","volume-title":"DeepIST: Deep Image-Based Spatio-Temporal Network for Travel Time Estimation (CIKM '19)","author":"Wang-Chien Lee Fu","unstructured":"Tao-yang Fu and Wang-Chien Lee . 2019. DeepIST: Deep Image-Based Spatio-Temporal Network for Travel Time Estimation (CIKM '19) . Association for Computing Machinery , New York, NY, USA , 69--78. https:\/\/doi.org\/10.1145\/3357384.3357870 10.1145\/3357384.3357870 Tao-yang Fu and Wang-Chien Lee. 2019. DeepIST: Deep Image-Based Spatio-Temporal Network for Travel Time Estimation (CIKM '19). Association for Computing Machinery, New York, NY, USA, 69--78. https:\/\/doi.org\/10.1145\/3357384.3357870"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2006.03.001"},{"key":"e_1_3_2_1_8_1","volume-title":"STGNN-TTE: Travel Time Estimation via Spatial--temporal Graph Neural Network. Future Gener. Comput. Syst","author":"Jin Guangyin","year":"2022","unstructured":"Guangyin Jin , Min Wang , Jinlei Zhang , Hengyu Sha , and Jincai Huang . 2022. STGNN-TTE: Travel Time Estimation via Spatial--temporal Graph Neural Network. Future Gener. Comput. Syst ., Vol. 126 , C ( jan 2022 ), 70--81. https:\/\/doi.org\/10.1016\/j.future.2021.07.012 10.1016\/j.future.2021.07.012 Guangyin Jin, Min Wang, Jinlei Zhang, Hengyu Sha, and Jincai Huang. 2022. STGNN-TTE: Travel Time Estimation via Spatial--temporal Graph Neural Network. Future Gener. Comput. Syst., Vol. 126, C (jan 2022), 70--81. https:\/\/doi.org\/10.1016\/j.future.2021.07.012"},{"key":"#cr-split#-e_1_3_2_1_9_1.1","doi-asserted-by":"crossref","unstructured":"Yaguang Li Kun Fu Zheng Wang Cyrus Shahabi Jieping Ye and Yan Liu. 2018. Multi-task Representation Learning for Travel Time Estimation. 1695--1704. https:\/\/doi.org\/10.1145\/3219819.3220033 10.1145\/3219819.3220033","DOI":"10.1145\/3219819.3220033"},{"key":"#cr-split#-e_1_3_2_1_9_1.2","doi-asserted-by":"crossref","unstructured":"Yaguang Li Kun Fu Zheng Wang Cyrus Shahabi Jieping Ye and Yan Liu. 2018. Multi-task Representation Learning for Travel Time Estimation. 1695--1704. https:\/\/doi.org\/10.1145\/3219819.3220033","DOI":"10.1145\/3219819.3220033"},{"key":"e_1_3_2_1_10_1","volume-title":"Graph Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. (07","author":"Li Yaguang","year":"2017","unstructured":"Yaguang Li , Rose Yu , Cyrus Shahabi , and Yan Liu . 2017. Graph Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. (07 2017 ). Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2017. Graph Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. (07 2017)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/3430915.3442433"},{"key":"e_1_3_2_1_12_1","volume-title":"Polestar: An Intelligent Routing Engine for National-Wide Public Transportation","author":"Liu Hao","year":"2022","unstructured":"Hao Liu , Ying Li , Yanjie Fu , Huaibo Mei , and Hui Xiong . 2022 . Polestar: An Intelligent Routing Engine for National-Wide Public Transportation . IEEE Transactions on Knowledge and Data Engineering ( 2022). Hao Liu, Ying Li, Yanjie Fu, Huaibo Mei, and Hui Xiong. 2022. Polestar: An Intelligent Routing Engine for National-Wide Public Transportation. IEEE Transactions on Knowledge and Data Engineering (2022)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.06.008"},{"key":"e_1_3_2_1_14_1","volume-title":"Expert Systems with Applications","volume":"120","author":"Petersen Niklas","year":"2018","unstructured":"Niklas Petersen , Filipe Rodrigues , and Francisco Pereira . 2018 . Multi-output Bus Travel Time Prediction with Convolutional LSTM Neural Network . Expert Systems with Applications , Vol. 120 (11 2018). https:\/\/doi.org\/10.1016\/j.eswa.2018.11.028 10.1016\/j.eswa.2018.11.028 Niklas Petersen, Filipe Rodrigues, and Francisco Pereira. 2018. Multi-output Bus Travel Time Prediction with Convolutional LSTM Neural Network. Expert Systems with Applications, Vol. 120 (11 2018). https:\/\/doi.org\/10.1016\/j.eswa.2018.11.028"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.11.023"},{"key":"e_1_3_2_1_16_1","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"Rong Yuecheng","unstructured":"Yuecheng Rong , Jun Liu , Zhilin Xu , Jian Ding , Chuangming Zhang , and Jiaxiang Gao . 2023. BusWTE: Realtime Bus Waiting Time Estimation of\u00a0GPS Missing via\u00a0Multi-task Learning . In Machine Learning and Knowledge Discovery in Databases . Springer Nature Switzerland , Cham , 554--570. Yuecheng Rong, Jun Liu, Zhilin Xu, Jian Ding, Chuangming Zhang, and Jiaxiang Gao. 2023. BusWTE: Realtime Bus Waiting Time Estimation of\u00a0GPS Missing via\u00a0Multi-task Learning. In Machine Learning and Knowledge Discovery in Databases. Springer Nature Switzerland, Cham, 554--570."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3210170"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415518"},{"key":"e_1_3_2_1_19_1","unstructured":"Petar Veli?kovi? Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. arxiv: 1710.10903 [stat.ML] Petar Veli?kovi? Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. arxiv: 1710.10903 [stat.ML]"},{"key":"e_1_3_2_1_20_1","volume-title":"Estimating Travel Time Based on Deep Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence","volume":"32","author":"Wang Dong","year":"2018","unstructured":"Dong Wang , Junbo Zhang , Wei Cao , Jian Li , and Yu Zheng . 2018 b. When Will You Arrive ? Estimating Travel Time Based on Deep Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 32 , 1 (Apr. 2018). https:\/\/doi.org\/10.1609\/aaai.v32i1.11877 10.1609\/aaai.v32i1.11877 Dong Wang, Junbo Zhang, Wei Cao, Jian Li, and Yu Zheng. 2018b. When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32, 1 (Apr. 2018). https:\/\/doi.org\/10.1609\/aaai.v32i1.11877"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Zheng Wang Kun Fu and Jieping Ye. 2018a. Learning to Estimate the Travel Time. 858--866. https:\/\/doi.org\/10.1145\/3219819.3219900 10.1145\/3219819.3219900 Zheng Wang Kun Fu and Jieping Ye. 2018a. Learning to Estimate the Travel Time. 858--866. https:\/\/doi.org\/10.1145\/3219819.3219900","DOI":"10.1145\/3219819.3219900"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.3354\/cr030079"},{"key":"e_1_3_2_1_23_1","volume-title":"Electronics","volume":"12","author":"Wu Lei","year":"2023","unstructured":"Lei Wu , Yong Tang , Pei Zhang , and Ying Zhou . 2023 . Spatio-Temporal Heterogeneous Graph Neural Networks for Estimating Time of Travel . Electronics , Vol. 12 , 6 (2023). https:\/\/doi.org\/10.3390\/electronics12061293 10.3390\/electronics12061293 Lei Wu, Yong Tang, Pei Zhang, and Ying Zhou. 2023. Spatio-Temporal Heterogeneous Graph Neural Networks for Estimating Time of Travel. Electronics, Vol. 12, 6 (2023). https:\/\/doi.org\/10.3390\/electronics12061293"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_25_1","first-page":"10","volume-title":"Frontiers of Data and Domputing","volume":"1","author":"Haifeng Wang Yanjun Ma Tian Wu","year":"2019","unstructured":"Tian Wu Haifeng Wang Yanjun Ma , Dianhai Yu . 2019 . PaddlePaddle: An Open-Source Deep Learning Platform from Industrial Practice . Frontiers of Data and Domputing , Vol. 1 , 1, Article 105 (2019), 10 pages. https:\/\/doi.org\/ 10 .11871\/jfdc.issn.2096.742X.2019.01.011 10.11871\/jfdc.issn.2096.742X.2019.01.011 Tian Wu Haifeng Wang Yanjun Ma, Dianhai Yu. 2019. PaddlePaddle: An Open-Source Deep Learning Platform from Industrial Practice. Frontiers of Data and Domputing, Vol. 1, 1, Article 105 (2019), 10 pages. https:\/\/doi.org\/10.11871\/jfdc.issn.2096.742X.2019.01.011"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Bing Yu Haoteng Yin and Zhanxing Zhu. 2018. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. 3634--3640. https:\/\/doi.org\/10.24963\/ijcai.2018\/505 10.24963\/ijcai.2018 Bing Yu Haoteng Yin and Zhanxing Zhu. 2018. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. 3634--3640. https:\/\/doi.org\/10.24963\/ijcai.2018\/505","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Hanyuan Zhang Hao Wu Weiwei Sun and Baihua Zheng. 2018b. DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision. 3655--3661. https:\/\/doi.org\/10.24963\/ijcai.2018\/508 10.24963\/ijcai.2018 Hanyuan Zhang Hao Wu Weiwei Sun and Baihua Zheng. 2018b. DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision. 3655--3661. https:\/\/doi.org\/10.24963\/ijcai.2018\/508","DOI":"10.24963\/ijcai.2018\/508"},{"key":"e_1_3_2_1_28_1","volume-title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. (03","author":"Zhang Jiani","year":"2018","unstructured":"Jiani Zhang , Xingjian Shi , Junyuan Xie , Hao Ma , Irwin King , and Dit-Yan Yeung . 2018a. GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. (03 2018 ). Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung. 2018a. GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. (03 2018)."},{"key":"e_1_3_2_1_29_1","volume-title":"STANN: A Spatio-Temporal Attentive Neural Network for Traffic Prediction","author":"Zhixiang He","year":"2018","unstructured":"He Zhixiang , Chi-Yin Chow , and Jia-Dong Zhang . 2018 . STANN: A Spatio-Temporal Attentive Neural Network for Traffic Prediction . IEEE Access, Vol . PP ( 12 2018), 1--1. https:\/\/doi.org\/10.1109\/ACCESS.2018.2888561 10.1109\/ACCESS.2018.2888561 He Zhixiang, Chi-Yin Chow, and Jia-Dong Zhang. 2018. STANN: A Spatio-Temporal Attentive Neural Network for Traffic Prediction. IEEE Access, Vol. PP (12 2018), 1--1. https:\/\/doi.org\/10.1109\/ACCESS.2018.2888561"}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","location":"Birmingham United Kingdom","acronym":"CIKM '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614730","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3614730","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:30Z","timestamp":1750178790000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614730"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":30,"alternative-id":["10.1145\/3583780.3614730","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3614730","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}