{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T14:52:09Z","timestamp":1777733529074,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":38,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62072125"],"award-info":[{"award-number":["No. 62072125"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Kong Research Grants Council","award":["No. 16202722, T43-513\/23-N-TRS"],"award-info":[{"award-number":["No. 16202722, T43-513\/23-N-TRS"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,21]]},"DOI":"10.1145\/3627673.3679600","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:34:11Z","timestamp":1729452851000},"page":"1266-1275","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Seeing the Forest for the Trees: Road-Level Insights Assisted Lane-Level Traffic Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5175-7667","authenticated-orcid":false,"given":"Shuhao","family":"Li","sequence":"first","affiliation":[{"name":"Fudan University &amp; Shanghai Key Laboratory of Data Science, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1656-5407","authenticated-orcid":false,"given":"Yue","family":"Cui","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong SAR, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6474-2695","authenticated-orcid":false,"given":"Jingyi","family":"Xu","sequence":"additional","affiliation":[{"name":"Fudan University &amp; Shanghai Key Laboratory of Data Science, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9529-7841","authenticated-orcid":false,"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong SAR, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0548-0130","authenticated-orcid":false,"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangzhou University &amp; GZHU-SCHB Intelligent Transportation Joint Lab, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6473-9272","authenticated-orcid":false,"given":"Weidong","family":"Yang","sequence":"additional","affiliation":[{"name":"Fudan University &amp; Zhuhai Fudan Innovation Research Institute, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6343-1455","authenticated-orcid":false,"given":"Xiaofang","family":"Zhou","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong SAR, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems","author":"Bai Lei","year":"2020","unstructured":"Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems, Vol. 33 (2020), 17804--17815."},{"key":"e_1_3_2_1_2_1","volume-title":"Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio.","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho, Bart Van Merri\u00ebnboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00107"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.14778\/3489496.3489503"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599533"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467430"},{"key":"e_1_3_2_1_7_1","volume-title":"Short-term prediction of lane-level traffic speeds: A fusion deep learning model. Transportation research part C: emerging technologies","author":"Gu Yuanli","year":"2019","unstructured":"Yuanli Gu, Wenqi Lu, Lingqiao Qin, Meng Li, and Zhuangzhuang Shao. 2019. Short-term prediction of lane-level traffic speeds: A fusion deep learning model. Transportation research part C: emerging technologies, Vol. 106 (2019), 1--16."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_1_9_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."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599453"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.25976"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1177\/0361198120911052"},{"key":"e_1_3_2_1_13_1","volume-title":"International Conference on Learning Representations.","author":"Kim Taesung","year":"2021","unstructured":"Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, and Jaegul Choo. 2021. Reversible instance normalization for accurate time-series forecasting against distribution shift. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/3208535"},{"key":"e_1_3_2_1_15_1","volume-title":"Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline. arXiv preprint arXiv:2403.14941","author":"Li Shuhao","year":"2024","unstructured":"Shuhao Li, Yue Cui, Jingyi Xu, Libin Li, Lingkai Meng, Weidong Yang, Fan Zhang, and Xiaofang Zhou. 2024. Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline. arXiv preprint arXiv:2403.14941 (2024)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615068"},{"key":"e_1_3_2_1_17_1","volume-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926","author":"Li Yaguang","year":"2017","unstructured":"Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)."},{"key":"e_1_3_2_1_18_1","volume-title":"Lane-level traffic speed forecasting: A novel mixed deep learning model","author":"Lu Wenqi","year":"2020","unstructured":"Wenqi Lu, Yikang Rui, and Bin Ran. 2020. Lane-level traffic speed forecasting: A novel mixed deep learning model. IEEE transactions on intelligent transportation systems, Vol. 23, 4 (2020), 3601--3612."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2977219"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/482"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2974575"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/0191-2615(84)90002-X"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.470"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2010.5625191"},{"key":"e_1_3_2_1_26_1","volume-title":"Mlp-mixer: An all-mlp architecture for vision. Advances in neural information processing systems","author":"Tolstikhin Ilya O","year":"2021","unstructured":"Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al. 2021. Mlp-mixer: An all-mlp architecture for vision. Advances in neural information processing systems, Vol. 34 (2021), 24261--24272."},{"key":"e_1_3_2_1_27_1","volume-title":"Lane-level traffic flow prediction based on dynamic graph generation. In 2021 IEEE 23rd Int Conf on High Performance Computing & Communications","author":"Wang Lingyun","year":"1802","unstructured":"Lingyun Wang, Guojiang Shen, Kaifeng Yu, Zhanhao Ji, and Xiangjie Kong. 2021. Lane-level traffic flow prediction based on dynamic graph generation. In 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC\/DSS\/SmartCity\/DependSys). IEEE, 1802--1809."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"e_1_3_2_1_30_1","volume-title":"Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121","author":"Wu Zonghan","year":"2019","unstructured":"Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)."},{"key":"e_1_3_2_1_31_1","volume-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint 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 preprint arXiv:1709.04875 (2017)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2878068"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3231959"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(01)00702-0"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/JRFID.2022.3217031"},{"key":"e_1_3_2_1_37_1","volume-title":"Lane-level heterogeneous traffic flow prediction: A spatiotemporal attention-based encoder--decoder model","author":"Zheng Yan","year":"2022","unstructured":"Yan Zheng, Wenquan Li, Wen Zheng, Chunjiao Dong, Shengyou Wang, and Qian Chen. 2022. Lane-level heterogeneous traffic flow prediction: A spatiotemporal attention-based encoder--decoder model. IEEE Intelligent Transportation Systems Magazine (2022)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.3390\/app12115340"}],"event":{"name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","location":"Boise ID USA","acronym":"CIKM '24","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 33rd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679600","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679600","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:23Z","timestamp":1750294703000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679600"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":38,"alternative-id":["10.1145\/3627673.3679600","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679600","relation":{},"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2024-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}