{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:50:03Z","timestamp":1777567803913,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":153,"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"}],"funder":[{"name":"Guangzhou-HKUST(GZ) Joint Funding Program","award":["2023A03J0008"],"award-info":[{"award-number":["2023A03J0008"]}]},{"name":"Education Bureau of Guangzhou Municipality"},{"name":"National Natural Science Foundation of China","award":["62102110,92370204"],"award-info":[{"award-number":["62102110,92370204"]}]},{"name":"National Key R&D Program of China","award":["2023YFF0725001"],"award-info":[{"award-number":["2023YFF0725001"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671453","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"6633-6643","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Urban Foundation Models: A Survey"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5085-5216","authenticated-orcid":false,"given":"Weijia","family":"Zhang","sequence":"first","affiliation":[{"name":"HKUST(GZ), Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1542-6149","authenticated-orcid":false,"given":"Jindong","family":"Han","sequence":"additional","affiliation":[{"name":"HKUST, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0974-4974","authenticated-orcid":false,"given":"Zhao","family":"Xu","sequence":"additional","affiliation":[{"name":"HKUST(GZ), Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1341-1602","authenticated-orcid":false,"given":"Hang","family":"Ni","sequence":"additional","affiliation":[{"name":"HKUST(GZ), Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4271-1567","authenticated-orcid":false,"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"HKUST(GZ) & HKUST, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6016-6465","authenticated-orcid":false,"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"HKUST(GZ) & HKUST, Guangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al.","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)."},{"key":"e_1_3_2_1_2_1","volume-title":"Can large language models be good path planners? a benchmark and investigation on spatial-temporal reasoning. arXiv preprint arXiv:2310.03249","author":"Aghzal Mohamed","year":"2023","unstructured":"Mohamed Aghzal, Erion Plaku, and Ziyu Yao. 2023. Can large language models be good path planners? a benchmark and investigation on spatial-temporal reasoning. arXiv preprint arXiv:2310.03249 (2023)."},{"key":"e_1_3_2_1_3_1","volume-title":"Sequential modeling enables scalable learning for large vision models. arXiv preprint arXiv:2312.00785","author":"Bai Yutong","year":"2023","unstructured":"Yutong Bai, Xinyang Geng, Karttikeya Mangalam, Amir Bar, Alan Yuille, Trevor Darrell, Jitendra Malik, and Alexei A Efros. 2023. Sequential modeling enables scalable learning for large vision models. arXiv preprint arXiv:2312.00785 (2023)."},{"key":"e_1_3_2_1_4_1","volume-title":"City Foundation Models for Learning General Purpose Representations from OpenStreetMap. arXiv e-prints","author":"Balsebre Pasquale","year":"2023","unstructured":"Pasquale Balsebre, Weiming Huang, Gao Cong, and Yi Li. 2023. City Foundation Models for Learning General Purpose Representations from OpenStreetMap. arXiv e-prints (2023), arXiv-2310."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589132.3625625"},{"key":"e_1_3_2_1_6_1","volume-title":"Nature","volume":"619","author":"Bi Kaifeng","year":"2023","unstructured":"Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, and Qi Tian. 2023. Accurate medium-range global weather forecasting with 3D neural networks. Nature, Vol. 619, 7970 (2023), 533--538."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.landurbplan.2021.104217"},{"key":"e_1_3_2_1_8_1","unstructured":"Rishi Bommasani Drew A Hudson Ehsan Adeli Russ Altman Simran Arora Sydney von Arx Michael S Bernstein Jeannette Bohg Antoine Bosselut Emma Brunskill et al. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2011.2119372"},{"key":"e_1_3_2_1_10_1","volume-title":"A billion-scale foundation model for remote sensing images. arXiv preprint arXiv:2304.05215","author":"Cha Keumgang","year":"2023","unstructured":"Keumgang Cha, Junghoon Seo, and Taekyung Lee. 2023. A billion-scale foundation model for remote sensing images. arXiv preprint arXiv:2304.05215 (2023)."},{"key":"e_1_3_2_1_11_1","first-page":"144","article-title":"Spatial Structure-Aware Road Network Embedding via Graph Contrastive Learning","volume":"26","author":"Chang Yanchuan","year":"2023","unstructured":"Yanchuan Chang, Egemen Tanin, Xin Cao, and Jianzhong Qi. 2023. Spatial Structure-Aware Road Network Embedding via Graph Contrastive Learning. Advances in Database Technology-EDBT, Vol. 26 (2023), 144--156.","journal-title":"Advances in Database Technology-EDBT"},{"key":"e_1_3_2_1_12_1","volume-title":"RSPrompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model. arXiv preprint arXiv:2306.16269","author":"Chen Keyan","year":"2023","unstructured":"Keyan Chen, Chenyang Liu, Hao Chen, Haotian Zhang, Wenyuan Li, Zhengxia Zou, and Zhenwei Shi. 2023. RSPrompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model. arXiv preprint arXiv:2306.16269 (2023)."},{"key":"e_1_3_2_1_13_1","volume-title":"Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey. arXiv preprint arXiv:2312.03014","author":"Chen Shengchao","year":"2023","unstructured":"Shengchao Chen, Guodong Long, Jing Jiang, Dikai Liu, and Chengqi Zhang. 2023. Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey. arXiv preprint arXiv:2312.03014 (2023)."},{"key":"e_1_3_2_1_14_1","volume-title":"Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data. arXiv preprint arXiv:2301.09152","author":"Chen Shengchao","year":"2023","unstructured":"Shengchao Chen, Guodong Long, Tao Shen, and Jing Jiang. 2023. Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data. arXiv preprint arXiv:2301.09152 (2023)."},{"key":"e_1_3_2_1_15_1","volume-title":"2023 d. Spatial-temporal Prompt Learning for Federated Weather Forecasting. arXiv preprint arXiv:2305.14244","author":"Chen Shengchao","year":"2023","unstructured":"Shengchao Chen, Guodong Long, Tao Shen, Tianyi Zhou, and Jing Jiang. 2023 d. Spatial-temporal Prompt Learning for Federated Weather Forecasting. arXiv preprint arXiv:2305.14244 (2023)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482293"},{"key":"e_1_3_2_1_17_1","volume-title":"2023 e. GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputation. arXiv preprint arXiv:2311.14332","author":"Chen Yakun","year":"2023","unstructured":"Yakun Chen, Xianzhi Wang, and Guandong Xu. 2023 e. GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputation. arXiv preprint arXiv:2311.14332 (2023)."},{"key":"e_1_3_2_1_18_1","volume-title":"Chatlaw: Open-source legal large language model with integrated external knowledge bases. arXiv preprint arXiv:2306.16092","author":"Cui Jiaxi","year":"2023","unstructured":"Jiaxi Cui, Zongjian Li, Yang Yan, Bohua Chen, and Li Yuan. 2023. Chatlaw: Open-source legal large language model with integrated external knowledge bases. arXiv preprint arXiv:2306.16092 (2023)."},{"key":"e_1_3_2_1_19_1","volume-title":"Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284","author":"Da Longchao","year":"2023","unstructured":"Longchao Da, Minchiuan Gao, Hao Mei, and Hua Wei. 2023. Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023)."},{"key":"e_1_3_2_1_20_1","volume-title":"Open-ti: Open traffic intelligence with augmented language model. International Journal of Machine Learning and Cybernetics","author":"Da Longchao","year":"2024","unstructured":"Longchao Da, Kuanru Liou, Tiejin Chen, Xuesong Zhou, Xiangyong Luo, Yezhou Yang, and Hua Wei. 2024. Open-ti: Open traffic intelligence with augmented language model. International Journal of Machine Learning and Cybernetics (2024), 1--26."},{"key":"e_1_3_2_1_21_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_22_1","first-page":"19822","article-title":"Cogview: Mastering text-to-image generation via transformers","volume":"34","author":"Ding Ming","year":"2021","unstructured":"Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, et al. 2021. Cogview: Mastering text-to-image generation via transformers. Advances in Neural Information Processing Systems, Vol. 34 (2021), 19822--19835.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591728"},{"key":"e_1_3_2_1_24_1","volume-title":"TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling. arXiv preprint arXiv:2402.02475","author":"Dong Jiaxiang","year":"2024","unstructured":"Jiaxiang Dong, Haixu Wu, Yuxuan Wang, Yunzhong Qiu, Li Zhang, Jianmin Wang, and Mingsheng Long. 2024. TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling. arXiv preprint arXiv:2402.02475 (2024)."},{"key":"e_1_3_2_1_25_1","volume-title":"SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling. arXiv preprint arXiv:2302.00861","author":"Dong Jiaxiang","year":"2023","unstructured":"Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jianmin Wang, and Mingsheng Long. 2023. SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling. arXiv preprint arXiv:2302.00861 (2023)."},{"key":"e_1_3_2_1_26_1","volume-title":"TTMs: Fast Multi-level Tiny Time Mixers for Improved Zero-shot and Few-shot Forecasting of Multivariate Time Series. arXiv preprint arXiv:2401.03955","author":"Ekambaram Vijay","year":"2024","unstructured":"Vijay Ekambaram, Arindam Jati, Nam H Nguyen, Pankaj Dayama, Chandra Reddy, Wesley M Gifford, and Jayant Kalagnanam. 2024. TTMs: Fast Multi-level Tiny Time Mixers for Improved Zero-shot and Few-shot Forecasting of Multivariate Time Series. arXiv preprint arXiv:2401.03955 (2024)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3412862"},{"key":"e_1_3_2_1_28_1","volume-title":"Towards Human-AI Collaborative Urban Science Research Enabled by Pre-trained Large Language Models. arXiv preprint arXiv:2305.11418","author":"Fu Jiayi","year":"2023","unstructured":"Jiayi Fu, Haoying Han, Xing Su, and Chao Fan. 2023. Towards Human-AI Collaborative Urban Science Research Enabled by Pre-trained Large Language Models. arXiv preprint arXiv:2305.11418 (2023)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3361741"},{"key":"e_1_3_2_1_30_1","volume-title":"UniTS: Building a Unified Time Series Model. arXiv preprint arXiv:2403.00131","author":"Gao Shanghua","year":"2024","unstructured":"Shanghua Gao, Teddy Koker, Owen Queen, Thomas Hartvigsen, Theodoros Tsiligkaridis, and Marinka Zitnik. 2024. UniTS: Building a Unified Time Series Model. arXiv preprint arXiv:2403.00131 (2024)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25546"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482169"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3545118"},{"key":"e_1_3_2_1_34_1","volume-title":"Language models represent space and time. arXiv preprint arXiv:2310.02207","author":"Gurnee Wes","year":"2023","unstructured":"Wes Gurnee and Max Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 (2023)."},{"key":"e_1_3_2_1_35_1","volume-title":"Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization. arXiv preprint arXiv:2302.00275","author":"Haas Lukas","year":"2023","unstructured":"Lukas Haas, Silas Alberti, and Michal Skreta. 2023. Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization. arXiv preprint arXiv:2302.00275 (2023)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3149815"},{"key":"e_1_3_2_1_37_1","volume-title":"Kill Two Birds with One Stone: A Multi-View Multi-Adversarial Learning Approach for Joint Air Quality and Weather Prediction","author":"Han Jindong","year":"2023","unstructured":"Jindong Han, Hao Liu, Hengshu Zhu, and Hui Xiong. 2023. Kill Two Birds with One Stone: A Multi-View Multi-Adversarial Learning Approach for Joint Air Quality and Weather Prediction. IEEE Transactions on Knowledge and Data Engineering (2023)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.14778\/3641204.3641217"},{"key":"e_1_3_2_1_39_1","volume-title":"Machine Learning for Urban Air Quality Analytics: A Survey. arXiv preprint arXiv:2310.09620","author":"Han Jindong","year":"2023","unstructured":"Jindong Han, Weijia Zhang, Hao Liu, and Hui Xiong. 2023. Machine Learning for Urban Air Quality Analytics: A Survey. arXiv preprint arXiv:2310.09620 (2023)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi13040133"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"e_1_3_2_1_42_1","volume-title":"Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685","author":"Hu Edward J","year":"2021","unstructured":"Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)."},{"key":"e_1_3_2_1_43_1","volume-title":"Self-supervised Pre-training for Robust and Generic Spatial-Temporal Representations. In IEEE International Conference on Data Mining. 150--159","author":"Hu Mingzhi","year":"2023","unstructured":"Mingzhi Hu, Zhuoyun Zhong, Xin Zhang, Yanhua Li, Yiqun Xie, Xiaowei Jia, Xun Zhou, and Jun Luo. 2023. Self-supervised Pre-training for Robust and Generic Spatial-Temporal Representations. In IEEE International Conference on Data Mining. 150--159."},{"key":"e_1_3_2_1_44_1","volume-title":"Large Foundation Models for Power Systems. arXiv preprint arXiv:2312.07044","author":"Huang Chenghao","year":"2023","unstructured":"Chenghao Huang, Siyang Li, Ruohong Liu, Hao Wang, and Yize Chen. 2023. Large Foundation Models for Power Systems. arXiv preprint arXiv:2312.07044 (2023)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539021"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Johannes Jakubik Sujit Roy CE Phillips Paolo Fraccaro Denys Godwin Bianca Zadrozny Daniela Szwarcman Carlos Gomes Gabby Nyirjesy Blair Edwards et al. 2023. Foundation Models for Generalist Geospatial Artificial Intelligence. arXiv preprint arXiv:2310.18660 (2023).","DOI":"10.2139\/ssrn.4804009"},{"key":"e_1_3_2_1_47_1","volume-title":"Evaluating the effectiveness of large language models in representing textual descriptions of geometry and spatial relations. arXiv preprint arXiv:2307.03678","author":"Ji Yuhan","year":"2023","unstructured":"Yuhan Ji and Song Gao. 2023. Evaluating the effectiveness of large language models in representing textual descriptions of geometry and spatial relations. arXiv preprint arXiv:2307.03678 (2023)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00070"},{"key":"e_1_3_2_1_49_1","unstructured":"Ming Jin Qingsong Wen Yuxuan Liang Chaoli Zhang Siqiao Xue Xue Wang James Zhang Yi Wang Haifeng Chen Xiaoli Li et al. 2023. Large models for time series and spatio-temporal data: A survey and outlook. arXiv preprint arXiv:2310.10196 (2023)."},{"key":"e_1_3_2_1_50_1","volume-title":"Position Paper: What Can Large Language Models Tell Us about Time Series Analysis. arXiv preprint arXiv:2402.02713","author":"Jin Ming","year":"2024","unstructured":"Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, and Qingsong Wen. 2024. Position Paper: What Can Large Language Models Tell Us about Time Series Analysis. arXiv preprint arXiv:2402.02713 (2024)."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539250"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599529"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-14280-7"},{"key":"e_1_3_2_1_54_1","volume-title":"Scaling laws for neural language models. arXiv preprint arXiv:2001.08361","author":"Kaplan Jared","year":"2020","unstructured":"Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361 (2020)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"crossref","unstructured":"Alexander Kirillov Eric Mintun Nikhila Ravi Hanzi Mao Chloe Rolland Laura Gustafson Tete Xiao Spencer Whitehead Alexander C Berg Wan-Yen Lo et al. 2023. Segment anything. arXiv preprint arXiv:2304.02643 (2023).","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"e_1_3_2_1_56_1","volume-title":"Machine Learning for Emergency Management: A Survey and Future Outlook. Proc","author":"Kyrkou Christos","year":"2022","unstructured":"Christos Kyrkou, Panayiotis Kolios, Theocharis Theocharides, and Marios Polycarpou. 2022. Machine Learning for Emergency Management: A Survey and Future Outlook. Proc. IEEE (2022)."},{"key":"e_1_3_2_1_57_1","volume-title":"Large Language Models as Traffic Signal Control Agents: Capacity and Opportunity. arXiv preprint arXiv:2312.16044","author":"Lai Siqi","year":"2023","unstructured":"Siqi Lai, Zhao Xu, Weijia Zhang, Hao Liu, and Hui Xiong. 2023. Large Language Models as Traffic Signal Control Agents: Capacity and Opportunity. arXiv preprint arXiv:2312.16044 (2023)."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599767"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557153"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00062"},{"key":"e_1_3_2_1_61_1","volume-title":"SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation. arXiv preprint arXiv:2210.12213","author":"Li Zekun","year":"2022","unstructured":"Zekun Li, Jina Kim, Yao-Yi Chiang, and Muhao Chen. 2022. SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation. arXiv preprint arXiv:2210.12213 (2022)."},{"key":"e_1_3_2_1_62_1","volume-title":"UrbanGPT: Spatio-Temporal Large Language Models. arXiv preprint arXiv:2403.00813","author":"Li Zhonghang","year":"2024","unstructured":"Zhonghang Li, Lianghao Xia, Jiabin Tang, Yong Xu, Lei Shi, Long Xia, Dawei Yin, and Chao Huang. 2024. UrbanGPT: Spatio-Temporal Large Language Models. arXiv preprint arXiv:2403.00813 (2024)."},{"key":"e_1_3_2_1_63_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Li Zhonghang","year":"2023","unstructured":"Zhonghang Li, Lianghao Xia, Yong Xu, and Chao Huang. 2023. GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks. Advances in Neural Information Processing Systems, Vol. 36 (2023)."},{"key":"e_1_3_2_1_64_1","volume-title":"GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding. arXiv preprint arXiv:2310.14478","author":"Li Zekun","year":"2023","unstructured":"Zekun Li, Wenxuan Zhou, Yao-Yi Chiang, and Muhao Chen. 2023. GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding. arXiv preprint arXiv:2310.14478 (2023)."},{"key":"e_1_3_2_1_65_1","volume-title":"Exploring large language models for human mobility prediction under public events. arXiv preprint arXiv:2311.17351","author":"Liang Yuebing","year":"2023","unstructured":"Yuebing Liang, Yichao Liu, Xiaohan Wang, and Zhan Zhao. 2023. Exploring large language models for human mobility prediction under public events. arXiv preprint arXiv:2311.17351 (2023)."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3347513"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16548"},{"key":"e_1_3_2_1_68_1","volume-title":"Spatial-temporal large language model for traffic prediction. arXiv preprint arXiv:2401.10134","author":"Liu Chenxi","year":"2024","unstructured":"Chenxi Liu, Sun Yang, Qianxiong Xu, Zhishuai Li, Cheng Long, Ziyue Li, and Rui Zhao. 2024. Spatial-temporal large language model for traffic prediction. arXiv preprint arXiv:2401.10134 (2024)."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599492"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-022-00748-y"},{"key":"e_1_3_2_1_71_1","volume-title":"How can large language models understand spatial-temporal data? arXiv preprint arXiv:2401.14192","author":"Liu Lei","year":"2024","unstructured":"Lei Liu, Shuo Yu, Runze Wang, Zhenxun Ma, and Yanming Shen. 2024. How can large language models understand spatial-temporal data? arXiv preprint arXiv:2401.14192 (2024)."},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3557915.3560939"},{"key":"e_1_3_2_1_73_1","volume-title":"Timer: Transformers for Time Series Analysis at Scale. arXiv preprint arXiv:2402.02368","author":"Liu Yong","year":"2024","unstructured":"Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, and Mingsheng Long. 2024. Timer: Transformers for Time Series Analysis at Scale. arXiv preprint arXiv:2402.02368 (2024)."},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583876"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539281"},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401090"},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/487"},{"key":"e_1_3_2_1_79_1","first-page":"11","article-title":"Steamlining Urban planning with AI tools","volume":"49","author":"Magee Dean","year":"2023","unstructured":"Dean Magee and Kevin Johnson. 2023. Steamlining Urban planning with AI tools. Planning News, Vol. 49, 5 (2023), 11--12.","journal-title":"Planning News"},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/3557915.3561043"},{"key":"e_1_3_2_1_81_1","unstructured":"Gengchen Mai Weiming Huang Jin Sun Suhang Song Deepak Mishra Ninghao Liu Song Gao Tianming Liu Gao Cong Yingjie Hu et al. 2023. On the opportunities and challenges of foundation models for geospatial artificial intelligence. arXiv preprint arXiv:2304.06798 (2023)."},{"key":"e_1_3_2_1_82_1","volume-title":"W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting. arXiv preprint arXiv:2304.08754","author":"Man Xin","year":"2023","unstructured":"Xin Man, Chenghong Zhang, Changyu Li, and Jie Shao. 2023. W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting. arXiv preprint arXiv:2304.08754 (2023)."},{"key":"e_1_3_2_1_83_1","volume-title":"Geollm: Extracting geospatial knowledge from large language models. arXiv preprint arXiv:2310.06213","author":"Manvi Rohin","year":"2023","unstructured":"Rohin Manvi, Samar Khanna, Gengchen Mai, Marshall Burke, David Lobell, and Stefano Ermon. 2023. Geollm: Extracting geospatial knowledge from large language models. arXiv preprint arXiv:2310.06213 (2023)."},{"key":"e_1_3_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1145\/3631937"},{"key":"e_1_3_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8306.2004.09402005.x"},{"key":"e_1_3_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1145\/3615886.3627745"},{"key":"e_1_3_2_1_87_1","volume-title":"Proceedings of the 40th International Conference on Machine Learning. 25904--25938","author":"Nguyen Tung","year":"2023","unstructured":"Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K Gupta, and Aditya Grover. 2023. ClimaX: A foundation model for weather and climate. In Proceedings of the 40th International Conference on Machine Learning. 25904--25938."},{"key":"e_1_3_2_1_88_1","volume-title":"UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction. arXiv preprint arXiv:2402.06861","author":"Ning Yansong","year":"2024","unstructured":"Yansong Ning and Hao Liu. 2024. UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction. arXiv preprint arXiv:2402.06861 (2024)."},{"key":"e_1_3_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.20944\/preprints201908.0154.v1"},{"key":"e_1_3_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330884"},{"key":"e_1_3_2_1_91_1","volume-title":"International conference on machine learning. PMLR, 8748--8763","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748--8763."},{"key":"e_1_3_2_1_92_1","volume-title":"TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models. arXiv preprint arXiv:2403.02221","author":"Ren Yilong","year":"2024","unstructured":"Yilong Ren, Yue Chen, Shuai Liu, Boyue Wang, Haiyang Yu, and Zhiyong Cui. 2024. TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models. arXiv preprint arXiv:2403.02221 (2024)."},{"key":"e_1_3_2_1_93_1","volume-title":"GPT4GEO: How a Language Model Sees the World's Geography. arXiv preprint arXiv:2306.00020","author":"Roberts Jonathan","year":"2023","unstructured":"Jonathan Roberts, Timo L\u00fcddecke, Sowmen Das, Kai Han, and Samuel Albanie. 2023. GPT4GEO: How a Language Model Sees the World's Geography. arXiv preprint arXiv:2306.00020 (2023)."},{"key":"e_1_3_2_1_94_1","volume-title":"Velma: Verbalization embodiment of llm agents for vision and language navigation in street view. arXiv preprint arXiv:2307.06082","author":"Schumann Raphael","year":"2023","unstructured":"Raphael Schumann, Wanrong Zhu, Weixi Feng, Tsu-Jui Fu, Stefan Riezler, and William Yang Wang. 2023. Velma: Verbalization embodiment of llm agents for vision and language navigation in street view. arXiv preprint arXiv:2307.06082 (2023)."},{"key":"e_1_3_2_1_95_1","unstructured":"Run Shao Cheng Yang Qiujun Li Qing Zhu Yongjun Zhang YanSheng Li Yu Liu Yong Tang Dapeng Liu Shizhong Yang et al. 2023. AllSpark: a multimodal spatiotemporal general model. arXiv preprint arXiv:2401.00546 (2023)."},{"key":"e_1_3_2_1_96_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539396"},{"key":"e_1_3_2_1_97_1","volume-title":"GeoSAM: Fine-tuning SAM with Sparse and Dense Visual Prompting for Automated Segmentation of Mobility Infrastructure. arXiv preprint arXiv:2311.11319","author":"Sultan Rafi Ibn","year":"2023","unstructured":"Rafi Ibn Sultan, Chengyin Li, Hui Zhu, Prashant Khanduri, Marco Brocanelli, and Dongxiao Zhu. 2023. GeoSAM: Fine-tuning SAM with Sparse and Dense Visual Prompting for Automated Segmentation of Mobility Infrastructure. arXiv preprint arXiv:2311.11319 (2023)."},{"key":"e_1_3_2_1_98_1","doi-asserted-by":"publisher","DOI":"10.1145\/3605554"},{"key":"e_1_3_2_1_99_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599839"},{"key":"e_1_3_2_1_100_1","unstructured":"Xiangguo Sun Hong Cheng Jia Li Bo Liu and Jihong Guan. 2023. All in One: Multi-Task Prompting for Graph Neural Networks. (2023)."},{"key":"e_1_3_2_1_101_1","doi-asserted-by":"crossref","unstructured":"Xian Sun Peijin Wang Wanxuan Lu Zicong Zhu Xiaonan Lu Qibin He Junxi Li Xuee Rong Zhujun Yang Hao Chang et al. 2022. RingMo: A remote sensing foundation model with masked image modeling. IEEE Transactions on Geoscience and Remote Sensing (2022).","DOI":"10.1109\/TGRS.2022.3194732"},{"key":"e_1_3_2_1_102_1","volume-title":"Sequence to sequence learning with neural networks. Advances in neural information processing systems","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems, Vol. 27 (2014)."},{"key":"e_1_3_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557294"},{"key":"e_1_3_2_1_104_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482004"},{"key":"e_1_3_2_1_105_1","unstructured":"Hugo Touvron Louis Martin Kevin Stone Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava Shruti Bhosale et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)."},{"key":"e_1_3_2_1_106_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2020.02.069"},{"key":"e_1_3_2_1_107_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43247-023-01084-x"},{"key":"e_1_3_2_1_108_1","volume-title":"Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning. arXiv preprint arXiv:2306.08094","author":"Villarreal Michael","year":"2023","unstructured":"Michael Villarreal, Bibek Poudel, and Weizi Li. 2023. Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning. arXiv preprint arXiv:2306.08094 (2023)."},{"key":"e_1_3_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57959-7"},{"key":"e_1_3_2_1_110_1","volume-title":"Towards automated urban planning: When generative and chatgpt-like ai meets urban planning. arXiv preprint arXiv:2304.03892","author":"Wang Dongjie","year":"2023","unstructured":"Dongjie Wang, Chang-Tien Lu, and Yanjie Fu. 2023. Towards automated urban planning: When generative and chatgpt-like ai meets urban planning. arXiv preprint arXiv:2304.03892 (2023)."},{"key":"e_1_3_2_1_111_1","volume-title":"SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track.","author":"Wang Di","year":"2023","unstructured":"Di Wang, Jing Zhang, Bo Du, Minqiang Xu, Lin Liu, Dacheng Tao, and Liangpei Zhang. 2023 d. SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track."},{"key":"e_1_3_2_1_112_1","first-page":"1","article-title":"Advancing plain vision transformer toward remote sensing foundation model","volume":"61","author":"Wang Di","year":"2022","unstructured":"Di Wang, Qiming Zhang, Yufei Xu, Jing Zhang, Bo Du, Dacheng Tao, and Liangpei Zhang. 2022. Advancing plain vision transformer toward remote sensing foundation model. IEEE Transactions on Geoscience and Remote Sensing, Vol. 61 (2022), 1--15.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_2_1_113_1","volume-title":"Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation. arXiv preprint arXiv:2402.14744","author":"Wang Jiawei","year":"2024","unstructured":"Jiawei Wang, Renhe Jiang, Chuang Yang, Zengqing Wu, Makoto Onizuka, Ryosuke Shibasaki, and Chuan Xiao. 2024. Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation. arXiv preprint arXiv:2402.14744 (2024)."},{"key":"e_1_3_2_1_114_1","volume-title":"Cross-city transfer learning for deep spatio-temporal prediction. arXiv preprint arXiv:1802.00386","author":"Wang Leye","year":"2018","unstructured":"Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, and Qiang Yang. 2018. Cross-city transfer learning for deep spatio-temporal prediction. arXiv preprint arXiv:1802.00386 (2018)."},{"key":"e_1_3_2_1_115_1","volume-title":"TransGPT: Multi-modal Generative Pre-trained Transformer for Transportation. arXiv preprint arXiv:2402.07233","author":"Wang Peng","year":"2024","unstructured":"Peng Wang, Xiang Wei, Fangxu Hu, and Wenjuan Han. 2024. TransGPT: Multi-modal Generative Pre-trained Transformer for Transportation. arXiv preprint arXiv:2402.07233 (2024)."},{"key":"e_1_3_2_1_116_1","volume-title":"Where would i go next? large language models as human mobility predictors. arXiv preprint arXiv:2308.15197","author":"Wang Xinglei","year":"2023","unstructured":"Xinglei Wang, Meng Fang, Zichao Zeng, and Tao Cheng. 2023. Where would i go next? large language models as human mobility predictors. arXiv preprint arXiv:2308.15197 (2023)."},{"key":"e_1_3_2_1_117_1","volume-title":"Building Transportation Foundation Model via Generative Graph Transformer. arXiv preprint arXiv:2305.14826","author":"Wang Xuhong","year":"2023","unstructured":"Xuhong Wang, Ding Wang, Liang Chen, and Yilun Lin. 2023. Building Transportation Foundation Model via Generative Graph Transformer. arXiv preprint arXiv:2305.14826 (2023)."},{"key":"e_1_3_2_1_118_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5450"},{"key":"e_1_3_2_1_119_1","unstructured":"Jason Wei Yi Tay Rishi Bommasani Colin Raffel Barret Zoph Sebastian Borgeaud Dani Yogatama Maarten Bosma Denny Zhou Donald Metzler et al. 2022. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682 (2022)."},{"key":"e_1_3_2_1_120_1","volume-title":"QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search. arXiv preprint arXiv:2306.06707","author":"Xie Jian","year":"2023","unstructured":"Jian Xie, Yidan Liang, Jingping Liu, Yanghua Xiao, Baohua Wu, and Shenghua Ni. 2023. QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search. arXiv preprint arXiv:2306.06707 (2023)."},{"key":"e_1_3_2_1_121_1","volume-title":"Travelplanner: A benchmark for real-world planning with language agents. arXiv preprint arXiv:2402.01622","author":"Xie Jian","year":"2024","unstructured":"Jian Xie, Kai Zhang, Jiangjie Chen, Tinghui Zhu, Renze Lou, Yuandong Tian, Yanghua Xiao, and Yu Su. 2024. Travelplanner: A benchmark for real-world planning with language agents. arXiv preprint arXiv:2402.01622 (2024)."},{"key":"e_1_3_2_1_122_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589132.3625616"},{"key":"e_1_3_2_1_123_1","volume-title":"Urban Generative Intelligence: A Foundational Platform for Agents in Embodied City Environment. arXiv preprint arXiv:2312.11813","author":"Xu Fengli","year":"2023","unstructured":"Fengli Xu, Jun Zhang, Chen Gao, Jie Feng, and Yong Li. 2023. Urban Generative Intelligence: A Foundational Platform for Agents in Embodied City Environment. arXiv preprint arXiv:2312.11813 (2023)."},{"key":"e_1_3_2_1_124_1","volume-title":"Promptcast: A new prompt-based learning paradigm for time series forecasting","author":"Xue Hao","year":"2023","unstructured":"Hao Xue and Flora D Salim. 2023. Promptcast: A new prompt-based learning paradigm for time series forecasting. IEEE Transactions on Knowledge and Data Engineering (2023)."},{"key":"e_1_3_2_1_125_1","doi-asserted-by":"publisher","DOI":"10.1145\/3557915.3561026"},{"key":"e_1_3_2_1_126_1","volume-title":"When Urban Region Profiling Meets Large Language Models. arXiv preprint arXiv:2310.18340","author":"Yan Yibo","year":"2023","unstructured":"Yibo Yan, Haomin Wen, Siru Zhong, Wei Chen, Haodong Chen, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang. 2023. When Urban Region Profiling Meets Large Language Models. arXiv preprint arXiv:2310.18340 (2023)."},{"key":"e_1_3_2_1_127_1","first-page":"1","article-title":"RingMo-SAM: A Foundation Model for Segment Anything in Multimodal Remote-Sensing Images","volume":"61","author":"Yan Zhiyuan","year":"2023","unstructured":"Zhiyuan Yan, Junxi Li, Xuexue Li, Ruixue Zhou, Wenkai Zhang, Yingchao Feng, Wenhui Diao, Kun Fu, and Xian Sun. 2023. RingMo-SAM: A Foundation Model for Segment Anything in Multimodal Remote-Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, Vol. 61 (2023), 1--16.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_2_1_128_1","doi-asserted-by":"publisher","DOI":"10.1145\/3339474"},{"key":"e_1_3_2_1_129_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/452"},{"key":"e_1_3_2_1_130_1","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2999--3010","author":"Yang Sean Bin","unstructured":"Sean Bin Yang, Jilin Hu, Chenjuan Guo, Bin Yang, and Christian S. Jensen. 2023. LightPath: Lightweight and Scalable Path Representation Learning. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2999--3010."},{"key":"e_1_3_2_1_131_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966345"},{"key":"e_1_3_2_1_132_1","unstructured":"Fanglong Yao Wanxuan Lu Heming Yang Liangyu Xu Chenglong Liu Leiyi Hu Hongfeng Yu Nayu Liu Chubo Deng Deke Tang et al. 2023. RingMo-sense: Remote sensing foundation model for spatiotemporal prediction via spatiotemporal evolution disentangling. IEEE Transactions on Geoscience and Remote Sensing (2023)."},{"key":"e_1_3_2_1_133_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 The world wide web conference. 2181--2191."},{"key":"e_1_3_2_1_134_1","volume-title":"UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction. arXiv preprint arXiv:2402.11838","author":"Yuan Yuan","year":"2024","unstructured":"Yuan Yuan, Jingtao Ding, Jie Feng, Depeng Jin, and Yong Li. 2024. UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction. arXiv preprint arXiv:2402.11838 (2024)."},{"key":"e_1_3_2_1_135_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449810"},{"key":"e_1_3_2_1_136_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tranpol.2024.03.006"},{"key":"e_1_3_2_1_137_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5471"},{"key":"e_1_3_2_1_138_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467187"},{"key":"e_1_3_2_1_139_1","volume-title":"Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach. In International Conference on Machine Learning. PMLR.","author":"Zhang Weijia","year":"2024","unstructured":"Weijia Zhang, Chenlong Yin, Hao Liu, Xiaofang Zhou, and Hui Xiong. 2024. Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach. In International Conference on Machine Learning. PMLR."},{"key":"e_1_3_2_1_140_1","volume-title":"Irregular traffic time series forecasting based on asynchronous spatio-temporal graph convolutional network. arXiv preprint arXiv:2308.16818","author":"Zhang Weijia","year":"2023","unstructured":"Weijia Zhang, Le Zhang, Jindong Han, Hao Liu, Jingbo Zhou, Yu Mei, and Hui Xiong. 2023. Irregular traffic time series forecasting based on asynchronous spatio-temporal graph convolutional network. arXiv preprint arXiv:2308.16818 (2023)."},{"key":"e_1_3_2_1_141_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614958"},{"key":"e_1_3_2_1_142_1","volume-title":"GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPT. arXiv preprint arXiv:2307.07930","author":"Zhang Yifan","year":"2023","unstructured":"Yifan Zhang, Cheng Wei, Shangyou Wu, Zhengting He, and Wenhao Yu. 2023. GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPT. arXiv preprint arXiv:2307.07930 (2023)."},{"key":"e_1_3_2_1_143_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615016"},{"key":"e_1_3_2_1_144_1","volume-title":"Parallel Transportation in TransVerse: From Foundation Models to DeCAST","author":"Zhao Chen","year":"2023","unstructured":"Chen Zhao, Xiao Wang, Yisheng Lv, Yonglin Tian, Yilun Lin, and Fei-Yue Wang. 2023. Parallel Transportation in TransVerse: From Foundation Models to DeCAST. IEEE Transactions on Intelligent Transportation Systems (2023)."},{"key":"e_1_3_2_1_145_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498444"},{"key":"e_1_3_2_1_146_1","volume-title":"Himtm: Hierarchical multi-scale masked time series modeling for long-term forecasting. arXiv preprint arXiv:2401.05012","author":"Zhao Shubao","year":"2024","unstructured":"Shubao Zhao, Ming Jin, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Qingsong Wen, and Yi Wang. 2024. Himtm: Hierarchical multi-scale masked time series modeling for long-term forecasting. arXiv preprint arXiv:2401.05012 (2024)."},{"key":"e_1_3_2_1_147_1","unstructured":"Wayne Xin Zhao Kun Zhou Junyi Li Tianyi Tang Xiaolei Wang Yupeng Hou Yingqian Min Beichen Zhang Junjie Zhang Zican Dong et al. 2023. A survey of large language models. arXiv preprint arXiv:2303.18223 (2023)."},{"key":"e_1_3_2_1_148_1","volume-title":"TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety. arXiv preprint arXiv:2307.15311","author":"Zheng Ou","year":"2023","unstructured":"Ou Zheng, Mohamed Abdel-Aty, Dongdong Wang, Chenzhu Wang, and Shengxuan Ding. 2023. TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety. arXiv preprint arXiv:2307.15311 (2023)."},{"key":"e_1_3_2_1_149_1","volume-title":"ChatGPT is on the horizon: Could a large language model be all we need for Intelligent Transportation? arXiv preprint arXiv:2303.05382","author":"Zheng Ou","year":"2023","unstructured":"Ou Zheng, Mohamed Abdel-Aty, Dongdong Wang, Zijin Wang, and Shengxuan Ding. 2023. ChatGPT is on the horizon: Could a large language model be all we need for Intelligent Transportation? arXiv preprint arXiv:2303.05382 (2023)."},{"key":"e_1_3_2_1_150_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2629592","article-title":"Urban computing: concepts, methodologies, and applications","volume":"5","author":"Zheng Yu","year":"2014","unstructured":"Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 5, 3 (2014), 1--55.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"e_1_3_2_1_151_1","doi-asserted-by":"crossref","unstructured":"Ce Zhou Qian Li Chen Li Jun Yu Yixin Liu Guangjing Wang Kai Zhang Cheng Ji Qiben Yan Lifang He et al. 2023. A comprehensive survey on pretrained foundation models: A history from bert to chatgpt. arXiv preprint arXiv:2302.09419 (2023).","DOI":"10.1007\/s13042-024-02443-6"},{"key":"e_1_3_2_1_152_1","volume-title":"Large Language Model for Participatory Urban Planning. arXiv preprint arXiv:2402.17161","author":"Zhou Zhilun","year":"2024","unstructured":"Zhilun Zhou, Yuming Lin, Depeng Jin, and Yong Li. 2024. Large Language Model for Participatory Urban Planning. arXiv preprint arXiv:2402.17161 (2024)."},{"key":"e_1_3_2_1_153_1","volume-title":"PlanGPT: Enhancing Urban Planning with Tailored Language Model and Efficient Retrieval. arXiv preprint arXiv:2402.19273","author":"Zhu He","year":"2024","unstructured":"He Zhu, Wenjia Zhang, Nuoxian Huang, Boyang Li, Luyao Niu, Zipei Fan, Tianle Lun, Yicheng Tao, Junyou Su, Zhaoya Gong, Chenyu Fang, and Xing Liu. 2024. PlanGPT: Enhancing Urban Planning with Tailored Language Model and Efficient Retrieval. arXiv preprint arXiv:2402.19273 (2024)."}],"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\/3637528.3671453","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671453","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:25Z","timestamp":1750291405000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671453"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":153,"alternative-id":["10.1145\/3637528.3671453","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671453","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"}}]}}