{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/urbanfm-inferring-fine-grained-urban-flows","title":"UrbanFM: Inferring Fine-Grained Urban Flows","arxiv_id":"1902.05377","date":"2019-02-06","proceeding":null,"authors":["Yuxuan Liang","Kun Ouyang","Lin Jing","Sijie Ruan","Ye Liu","Junbo Zhang","David S. Rosenblum","Yu Zheng"],"abstract":"Urban flow monitoring systems play important roles in smart city efforts\naround the world. However, the ubiquitous deployment of monitoring devices,\nsuch as CCTVs, induces a long-lasting and enormous cost for maintenance and\noperation. This suggests the need for a technology that can reduce the number\nof deployed devices, while preventing the degeneration of data accuracy and\ngranularity. In this paper, we aim to infer the real-time and fine-grained\ncrowd flows throughout a city based on coarse-grained observations. This task\nis challenging due to two reasons: the spatial correlations between coarse- and\nfine-grained urban flows, and the complexities of external impacts. To tackle\nthese issues, we develop a method entitled UrbanFM based on deep neural\nnetworks. Our model consists of two major parts: 1) an inference network to\ngenerate fine-grained flow distributions from coarse-grained inputs by using a\nfeature extraction module and a novel distributional upsampling module; 2) a\ngeneral fusion subnet to further boost the performance by considering the\ninfluences of different external factors. Extensive experiments on two\nreal-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness\nand efficiency of our method compared to seven baselines, demonstrating the\nstate-of-the-art performance of our approach on the fine-grained urban flow\ninference problem.","url_abs":"http://arxiv.org/abs/1902.05377v1","url_pdf":"http://arxiv.org/pdf/1902.05377v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"urbanfm-inferring-fine-grained-urban-flows","repo_url":"https://github.com/yoshall/UrbanFM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"fine-grained-urban-flow-inference","task_name":"Fine-Grained Urban Flow Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P1","model":"UrbanFM","rank_in_archive_order":2,"of":9,"metrics":{"MAE":"2.011","MAPE":"0.327","MSE":"15.6025"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P1","model":"UrbanFM-ne","rank_in_archive_order":3,"of":9,"metrics":{"MAE":"2.047","MAPE":"0.332","MSE":"16.1202"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P1","model":"DeepSD","rank_in_archive_order":4,"of":9,"metrics":{"MAE":"2.368","MAPE":"0.614","MSE":"17.2723"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P1","model":"VDSR","rank_in_archive_order":5,"of":9,"metrics":{"MAE":"2.213","MAPE":"0.467","MSE":"17.2972"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P1","model":"SRResNet","rank_in_archive_order":6,"of":9,"metrics":{"MAE":"2.457","MAPE":"0.713","MSE":"17.3388"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P1","model":"ESPCN","rank_in_archive_order":7,"of":9,"metrics":{"MAE":"2.497","MAPE":"0.732","MSE":"17.6904"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P1","model":"SRCNN","rank_in_archive_order":8,"of":9,"metrics":{"MAE":"2.491","MAPE":"0.714","MSE":"18.4642"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P1","model":"HA","rank_in_archive_order":9,"of":9,"metrics":{"MAE":"2.251","MAPE":"0.336","MSE":"22.4770"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj-1","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P2","model":"UrbanFM","rank_in_archive_order":2,"of":3,"metrics":{"MAE":"2.224","MAPE":"0.313","MSE ":"18.7402"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj-1","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P2","model":"UrbanFM-ne","rank_in_archive_order":3,"of":3,"metrics":{"MAE":"2.258","MAPE":"0.320","MSE ":"19.2369"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj-2","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P3","model":"UrbanFM","rank_in_archive_order":2,"of":2,"metrics":{"MAE":"2.318","MAPE":"0.315","MSE":"20.2140"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj-3","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P4","model":"UrbanFM","rank_in_archive_order":2,"of":3,"metrics":{"MAE":"1.815","MAPE":"0.308","MSE ":"12.2570"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj-3","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P4","model":"UrbanFM-ne","rank_in_archive_order":3,"of":3,"metrics":{"MAE":"1.845","MAPE":"0.309","MSE ":"12.666"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1902.05377","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}