{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T22:39:12Z","timestamp":1774910352679,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:00:00Z","timestamp":1730160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"Army Research Laboratory","doi-asserted-by":"publisher","award":["W911NF-23-2-0014"],"award-info":[{"award-number":["W911NF-23-2-0014"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CNS-2136948"],"award-info":[{"award-number":["CNS-2136948"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,29]]},"DOI":"10.1145\/3678717.3691308","type":"proceedings-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T06:29:21Z","timestamp":1732256961000},"page":"384-395","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Harnessing LLMs for Cross-City OD Flow Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6416-5742","authenticated-orcid":false,"given":"Chenyang","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of North Texas, Denton, Texas, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6729-2879","authenticated-orcid":false,"given":"Xinpeng","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of North Texas, Denton, Texas, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0575-0156","authenticated-orcid":false,"given":"Yan","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of North Texas, Denton, Texas, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3387-9758","authenticated-orcid":false,"given":"Chenxi","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of North Texas, Denton, Texas, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21217080"},{"key":"e_1_3_2_1_2_1","volume-title":"A theory of learning from different domains. Machine learning 79","author":"Ben-David Shai","year":"2010","unstructured":"Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010. A theory of learning from different domains. Machine learning 79 (2010), 151--175."},{"key":"e_1_3_2_1_3_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877--1901."},{"key":"e_1_3_2_1_4_1","volume-title":"Spatial Attention Based Grid Representation Learning For Predicting Origin-Destination Flow. In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 485--494","author":"Cai Mingfei","year":"2022","unstructured":"Mingfei Cai, Yanbo Pang, and Yoshihide Sekimoto. 2022. Spatial Attention Based Grid Representation Learning For Predicting Origin-Destination Flow. In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 485--494."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12544-022-00562-1"},{"key":"e_1_3_2_1_6_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In North American","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In North American Chapter of the Association for Computational Linguistics."},{"key":"e_1_3_2_1_7_1","first-page":"641","article-title":"Modelling public transport passenger flows in the era of intelligent transport systems","volume":"10","author":"Gentile Guido","year":"2016","unstructured":"Guido Gentile and Klaus N\u00f6kel. 2016. Modelling public transport passenger flows in the era of intelligent transport systems. Springer Tracts on Transportation and Traffic 10 (2016), 641.","journal-title":"Springer Tracts on Transportation and Traffic"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","unstructured":"Guangzeng Han Weisi Liu Xiaolei Huang and Brian Borsari. 2024. Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts. 392--401 pages. https:\/\/doi.org\/10.1109\/ICHI61247.2024.00057","DOI":"10.1109\/ICHI61247.2024.00057"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3183570"},{"key":"e_1_3_2_1_10_1","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:2106.09685 [cs.CL]"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Liping Huang Yongjian Yang Xuehua Zhao Hepeng Gao Limin Yu et al. 2018. Mining the Relationship between Spatial Mobility Patterns and POIs. Wireless Communications and Mobile Computing 2018 (2018).","DOI":"10.1155\/2018\/4392524"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.05.114"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Dongjun Kang Joonsuk Park Yohan Jo and JinYeong Bak. 2023. From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models. arXiv:2310.17857 [cs.CL]","DOI":"10.18653\/v1\/2023.emnlp-main.961"},{"key":"e_1_3_2_1_14_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0096180"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00515"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0140152"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2015.12.008"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5425"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.3758\/BF03194554"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2019.101354"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2020.101552"},{"key":"e_1_3_2_1_23_1","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans Ilya Sutskever et al. 2018. Improving language understanding by generative pre-training. (2018)."},{"key":"e_1_3_2_1_24_1","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei Ilya Sutskever et al. 2019. Language models are unsupervised multitask learners. OpenAI blog 1 8 (2019) 9."},{"key":"e_1_3_2_1_25_1","volume-title":"Predicting commuter flows in spatial networks using a radiation model based on temporal ranges. Nature communications 5, 1","author":"Ren Yihui","year":"2014","unstructured":"Yihui Ren, M\u00e1ria Ercsey-Ravasz, Pu Wang, Marta C Gonz\u00e1lez, and Zolt\u00e1n Toroczkai. 2014. Predicting commuter flows in spatial networks using a radiation model based on temporal ranges. Nature communications 5, 1 (2014), 1--9."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209811.3209868"},{"key":"e_1_3_2_1_27_1","volume-title":"Complexity-aware large scale origin-destination network generation via diffusion model. arXiv preprint arXiv:2306.04873","author":"Rong Can","year":"2023","unstructured":"Can Rong, Jingtao Ding, Zhicheng Liu, and Yong Li. 2023. Complexity-aware large scale origin-destination network generation via diffusion model. arXiv preprint arXiv:2306.04873 (2023)."},{"key":"e_1_3_2_1_28_1","volume-title":"GODDAG: generating origin-destination flow for new cities via domain adversarial training","author":"Rong Can","year":"2023","unstructured":"Can Rong, Jie Feng, and Jingtao Ding. 2023. GODDAG: generating origin-destination flow for new cities via domain adversarial training. IEEE Transactions on Knowledge and Data Engineering (2023)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3183743"},{"key":"e_1_3_2_1_30_1","volume-title":"Demand estimation for public transport network planning","author":"Sun Wenzhe","unstructured":"Wenzhe Sun and Jan-Dirk Schm\u00f6cker. 2021. Demand estimation for public transport network planning. In The Routledge Handbook of Public Transport. Routledge, 289--305."},{"key":"e_1_3_2_1_31_1","volume-title":"\u0141 ukasz Kaiser, and Illia Polosukhin","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141 ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"e_1_3_2_1_32_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00543"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2019.101368"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2017.8057019"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3135898"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3135898"}],"event":{"name":"SIGSPATIAL '24: The 32nd ACM International Conference on Advances in Geographic Information Systems","location":"Atlanta GA USA","acronym":"SIGSPATIAL '24","sponsor":["SIGSPATIAL ACM Special Interest Group on Spatial Information"]},"container-title":["Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3678717.3691308","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3678717.3691308","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T10:42:20Z","timestamp":1755859340000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3678717.3691308"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,29]]},"references-count":37,"alternative-id":["10.1145\/3678717.3691308","10.1145\/3678717"],"URL":"https:\/\/doi.org\/10.1145\/3678717.3691308","relation":{},"subject":[],"published":{"date-parts":[[2024,10,29]]},"assertion":[{"value":"2024-11-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}