{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:10:40Z","timestamp":1775146240623,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":55,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"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":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671578","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"5351-5362","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":103,"title":["UrbanGPT: Spatio-Temporal Large Language Models"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3977-1334","authenticated-orcid":false,"given":"Zhonghang","family":"Li","sequence":"first","affiliation":[{"name":"South China University of Technology &amp; The University of Hong Kong, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0725-2211","authenticated-orcid":false,"given":"Lianghao","family":"Xia","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Hong Kong SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7002-3585","authenticated-orcid":false,"given":"Jiabin","family":"Tang","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Hong Kong SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7183-3155","authenticated-orcid":false,"given":"Yong","family":"Xu","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8043-7224","authenticated-orcid":false,"given":"Lei","family":"Shi","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7536-6241","authenticated-orcid":false,"given":"Long","family":"Xia","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0684-6205","authenticated-orcid":false,"given":"Dawei","family":"Yin","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2062-1512","authenticated-orcid":false,"given":"Chao","family":"Huang","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Hong Kong SAR, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Lei Bai Lina Yao Can Li Xianzhi Wang and Can Wang. 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. In NeurIPS. 17804--17815."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Maciej Besta Nils Blach Ales Kubicek Robert Gerstenberger Michal Podstawski Lukas Gianinazzi et al. 2024. Graph of Thoughts: Solving Elaborate Problems with Large Language Models. (2024) 17682--17690.","DOI":"10.1609\/aaai.v38i16.29720"},{"key":"e_1_3_2_2_3_1","unstructured":"Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry et al. 2020. Language Models Are Few-Shot Learners. In NeurIPS. 1877--1901."},{"key":"e_1_3_2_2_4_1","unstructured":"Ziwei Chai Tianjie Zhang Liang Wu Kaiqiao Han Xiaohai Hu Xuanwen Huang and Yang Yang. 2023. GraphLLM: Boosting Graph Reasoning Ability of Large Language Model. arxiv: 2310.05845"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Razvan-Gabriel Cirstea Bin Yang Chenjuan Guo Tung Kieu and Shirui Pan. 2022. Towards Spatio-Temporal Aware Traffic Time Series Forecasting. In ICDE. 2900--2913.","DOI":"10.1109\/ICDE53745.2022.00262"},{"key":"e_1_3_2_2_6_1","unstructured":"Longchao Da Kuanru Liou Tiejin Chen Xuesong Zhou Xiangyong Luo Yezhou Yang and Hua Wei. 2023. Open-TI: Open Traffic Intelligence with Augmented Language Model. arxiv: 2401.00211"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186058"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Shengnan Guo Youfang Lin Ning Feng Chao Song and Huaiyu Wan. 2019. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. In AAAI. 922--929.","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Liangzhe Han Bowen Du Leilei Sun Yanjie Fu Yisheng Lv and Hui Xiong. 2021. Dynamic and Multi-Faceted Spatio-Temporal Deep Learning for Traffic Speed Forecasting. In KDD. 547--555.","DOI":"10.1145\/3447548.3467275"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Jesse Harte Wouter Zorgdrager Panos Louridas Asterios Katsifodimos Dietmar Jannach and Marios Fragkoulis. 2023. Leveraging large language models for sequential recommendation. In Recsys. 1096--1102.","DOI":"10.1145\/3604915.3610639"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Chao Huang Junbo Zhang Yu Zheng and Nitesh V Chawla. 2018. DeepCrime: Attentive hierarchical recurrent networks for crime prediction. In CIKM. 1423--1432.","DOI":"10.1145\/3269206.3271793"},{"key":"e_1_3_2_2_12_1","volume-title":"Wayne Xin Zhao, and Jingyuan Wang","author":"Jiang Jiawei","year":"2023","unstructured":"Jiawei Jiang, Chengkai Han, Wayne Xin Zhao, and Jingyuan Wang. 2023. PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction. In AAAI. 4365--4373."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"Yilun Jin Kai Chen and Qiang Yang. 2022. Selective cross-city transfer learning for traffic prediction via source city region re-weighting. In KDD. 731--741.","DOI":"10.1145\/3534678.3539250"},{"key":"e_1_3_2_2_14_1","unstructured":"Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2018. Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In ICLR."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"crossref","unstructured":"Zhonghang Li Chao Huang Lianghao Xia Yong Xu and Jian Pei. 2022. Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction. In ICDE. 2984--2996.","DOI":"10.1109\/ICDE53745.2022.00269"},{"key":"e_1_3_2_2_16_1","unstructured":"Zhonghang Li Lianghao Xia Yong Xu and Chao Huang. 2023. GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks. In Advances in Neural Information Processing Systems. 70229--70246."},{"key":"e_1_3_2_2_17_1","unstructured":"Zhonghang Li Lianghao Xia Yong Xu and Chao Huang. 2024. FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction. arxiv: 2405.17898"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330646"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"Binbing Liao Jingqing Zhang Chao Wu Douglas McIlwraith Tong Chen Shengwen Yang Yike Guo and Fei Wu. 2018. Deep sequence learning with auxiliary information for traffic prediction. In KDD. 537--546.","DOI":"10.1145\/3219819.3219895"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Bin Lu Xiaoying Gan Weinan Zhang Huaxiu Yao Luoyi Fu and Xinbing Wang. 2022. Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer. In KDD. 1162--1172.","DOI":"10.1145\/3534678.3539281"},{"key":"e_1_3_2_2_21_1","volume-title":"A survey on deep learning for human mobilityACM Computing Surveys (CSUR)","author":"Luca Massimiliano","year":"2021","unstructured":"Massimiliano Luca, Gianni Barlacchi, Bruno Lepri, and Luca Pappalardo. 2021. A survey on deep learning for human mobilityACM Computing Surveys (CSUR), Vol. 55, 1 (2021), 1--44."},{"key":"e_1_3_2_2_22_1","unstructured":"Long Ouyang Jeffrey Wu Xu Jiang Diogo Almeida Carroll Wainwright Pamela Mishkin et al. 2022. Training language models to follow instructions with human feedback. In NeurIPS. 27730--27744."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Zheyi Pan Yuxuan Liang Weifeng Wang et al. 2019. Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning. In KDD.","DOI":"10.1145\/3292500.3330884"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Xubin Ren Wei Wei Lianghao Xia Lixin Su Suqi Cheng Junfeng Wang Dawei Yin and Chao Huang. 2024. Representation Learning with Large Language Models for Recommendation. In WWW. 3464--3475.","DOI":"10.1145\/3589334.3645458"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Zezhi Shao Zhao Zhang Fei Wang and Yongjun Xu. 2022. Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting. In KDD. 1567--1577.","DOI":"10.1145\/3534678.3539396"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","unstructured":"Sheng Shen Shijia Yang Tianjun Zhang Bohan Zhai Joseph E. Gonzalez Kurt Keutzer and Trevor Darrell. 2024. Multitask Vision-Language Prompt Tuning. In WACV. 5656--5667.","DOI":"10.1109\/WACV57701.2024.00556"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Chao Song Youfang Lin Shengnan Guo and Huaiyu Wan. 2020. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting. In AAAI. 914--921.","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Jiabin Tang Yuhao Yang Wei Wei Lei Shi Lixin Su Suqi Cheng Dawei Yin and Chao Huang. 2024. GraphGPT: Graph Instruction Tuning for Large Language Models. arxiv: 2310.13023","DOI":"10.1145\/3626772.3657775"},{"key":"e_1_3_2_2_29_1","unstructured":"Hugo Touvron Thibaut Lavril Gautier Izacard Xavier Martinet Marie-Anne Lachaux et al. 2023. LLaMA: Open and Efficient Foundation Language Models. arxiv: 2302.13971"},{"key":"e_1_3_2_2_30_1","unstructured":"Hugo Touvron Louis Martin Kevin Stone Peter Albert Amjad Almahairi Yasmine Babaei et al. 2023. Llama 2: Open Foundation and Fine-Tuned Chat Models. arxiv: 2307.09288"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Beibei Wang Youfang Lin Shengnan Guo and Huaiyu Wan. 2021. GSNet: Learning Spatial-Temporal Correlations from Geographical and Semantic Aspects for Traffic Accident Risk Forecasting. AAAI 4402--4409.","DOI":"10.1609\/aaai.v35i5.16566"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Binwu Wang Yudong Zhang Xu Wang Pengkun Wang Zhengyang Zhou Lei Bai and Yang Wang. 2023. Pattern expansion and consolidation on evolving graphs for continual traffic prediction. In KDD. 2223--2232.","DOI":"10.1145\/3580305.3599463"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Hongjian Wang Daniel Kifer Corina Graif and Zhenhui Li. 2016. Crime rate inference with big data. In KDD. 635--644.","DOI":"10.1145\/2939672.2939736"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Jingyuan Wang Jiawei Jiang Wenjun Jiang Chao Li and Wayne Xin Zhao. 2021. LibCity: An Open Library for Traffic Prediction. In SIGSPATIAL. 145--148.","DOI":"10.1145\/3474717.3483923"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Xiaoyang Wang Yao Ma Yiqi Wang Wei Jin Xin Wang Jiliang Tang Caiyan Jia and Jian Yu. 2020. Traffic Flow Prediction via Spatial Temporal Graph Neural Network. In WWW. 1082--1092.","DOI":"10.1145\/3366423.3380186"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"crossref","unstructured":"Wei Wei Xubin Ren Jiabin Tang Qinyong Wang Lixin Su Suqi Cheng Junfeng Wang Dawei Yin and Chao Huang. 2024. LLMRec: Large Language Models with Graph Augmentation for Recommendation. arxiv: 2311.00423","DOI":"10.1145\/3616855.3635853"},{"key":"e_1_3_2_2_37_1","unstructured":"Haixu Wu Tengge Hu Yong Liu Hang Zhou Jianmin Wang and Mingsheng Long. 2023. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. In ICLR."},{"key":"e_1_3_2_2_38_1","unstructured":"Zonghan Wu Shirui Pan Guodong Long Jing Jiang Xiaojun Chang and Chengqi Zhang. 2020. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. In KDD. 753--763."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Zonghan Wu Shirui Pan Guodong Long Jing Jiang and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. In IJCAI. 1907--1913.","DOI":"10.24963\/ijcai.2019\/264"},{"key":"e_1_3_2_2_40_1","unstructured":"Huaxiu Yao Yiding Liu Ying Wei Xianfeng Tang and Zhenhui Li. 2019. Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. In WWW. 2181--2191."},{"key":"e_1_3_2_2_41_1","unstructured":"Huaxiu Yao Fei Wu Jintao Ke Xianfeng Tang Yitian Jia Siyu Lu Pinghua Gong Jieping Ye Didi Chuxing and Zhenhui Li. 2018. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction. In AAAI."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"crossref","unstructured":"Junchen Ye Leilei Sun Bowen Du Yanjie Fu and Hui Xiong. 2021. Coupled Layer-wise Graph Convolution for Transportation Demand Prediction. In AAAI. 4617--4625.","DOI":"10.1609\/aaai.v35i5.16591"},{"key":"e_1_3_2_2_43_1","unstructured":"Xiuwen Yi Yu Zheng Junbo Zhang and Tianrui Li. 2016. ST-MVL: filling missing values in geo-sensory time series data. In IJCAI. 2704--2710."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"crossref","unstructured":"Bing Yu Haoteng Yin et al. 2018. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In IJCAI. 3634--3640.","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_2_45_1","volume-title":"Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting. In SDM. 777--785.","author":"Yu Rose","year":"2017","unstructured":"Rose Yu, Yaguang Li, Cyrus Shahabi, Ugur Demiryurek, and Yan Liu. 2017. Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting. In SDM. 777--785."},{"key":"e_1_3_2_2_46_1","unstructured":"Aohan Zeng Xiao Liu Zhengxiao Du Zihan Wang Hanyu Lai Ming Ding Zhuoyi Yang Yifan Xu Wendi Zheng Xiao Xia et al. 2023. GLM-130B: An Open Bilingual Pre-trained Model. In ICLR."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"crossref","unstructured":"Junbo Zhang Yu Zheng and Dekang Qi. 2017. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In AAAI.","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"crossref","unstructured":"Qianru Zhang Chao Huang Lianghao Xia Zheng Wang Zhonghang Li and Siuming Yiu. 2023. Automated Spatio-Temporal Graph Contrastive Learning. In WWW. 295--305.","DOI":"10.1145\/3543507.3583304"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"crossref","unstructured":"Yusheng Zhao Xiao Luo Wei Ju Chong Chen Xian-Sheng Hua and Ming Zhang. 2023. Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting. In ICDE. 2303--2316.","DOI":"10.1109\/ICDE55515.2023.00178"},{"key":"e_1_3_2_2_51_1","volume-title":"GMAN: A Graph Multi-Attention Network for Traffic Prediction. In AAAI. 1234--1241.","author":"Zheng Chuanpan","year":"2020","unstructured":"Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. 2020. GMAN: A Graph Multi-Attention Network for Traffic Prediction. In AAAI. 1234--1241."},{"key":"e_1_3_2_2_52_1","unstructured":"Lianmin Zheng Wei-Lin Chiang Ying Sheng Siyuan Zhuang Zhanghao Wu Yonghao Zhuang et al. 2023. Judging LLM-as-a-judge with MT-Bench and Chatbot Arena. arxiv: 2306.05685"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01653-1"},{"key":"e_1_3_2_2_54_1","volume-title":"Liang Sun, and Rong Jin.","author":"Zhou Tian","year":"2023","unstructured":"Tian Zhou, Peisong Niu, xue wang, Liang Sun, and Rong Jin. 2023. One Fits All: Power General Time Series Analysis by Pretrained LM. In NeurIPS. 43322--43355."},{"key":"e_1_3_2_2_55_1","unstructured":"Deyao Zhu Jun Chen Xiaoqian Shen Xiang Li and Mohamed Elhoseiny. 2023. MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models. arxiv: 2304.10592"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671578","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671578","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:19Z","timestamp":1750291459000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":55,"alternative-id":["10.1145\/3637528.3671578","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671578","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}