{"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/deep-spatio-temporal-residual-networks-for","title":"Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction","arxiv_id":"1610.00081","date":"2016-10-01","proceeding":null,"authors":["Junbo Zhang","Yu Zheng","Dekang Qi"],"abstract":"Forecasting the flow of crowds is of great importance to traffic management\nand public safety, yet a very challenging task affected by many complex\nfactors, such as inter-region traffic, events and weather. In this paper, we\npropose a deep-learning-based approach, called ST-ResNet, to collectively\nforecast the in-flow and out-flow of crowds in each and every region through a\ncity. We design an end-to-end structure of ST-ResNet based on unique properties\nof spatio-temporal data. More specifically, we employ the framework of the\nresidual neural networks to model the temporal closeness, period, and trend\nproperties of the crowd traffic, respectively. For each property, we design a\nbranch of residual convolutional units, each of which models the spatial\nproperties of the crowd traffic. ST-ResNet learns to dynamically aggregate the\noutput of the three residual neural networks based on data, assigning different\nweights to different branches and regions. The aggregation is further combined\nwith external factors, such as weather and day of the week, to predict the\nfinal traffic of crowds in each and every region. We evaluate ST-ResNet based\non two types of crowd flows in Beijing and NYC, finding that its performance\nexceeds six well-know methods.","url_abs":"http://arxiv.org/abs/1610.00081v2","url_pdf":"http://arxiv.org/pdf/1610.00081v2.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":"deep-spatio-temporal-residual-networks-for","repo_url":"https://github.com/BruceBinBoxing/ST-ResNet-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-spatio-temporal-residual-networks-for","repo_url":"https://github.com/duyhlzu/GMG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"deep-spatio-temporal-residual-networks-for","repo_url":"https://github.com/isds-neu/percnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-spatio-temporal-residual-networks-for","repo_url":"https://github.com/snehasinghania/STResNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deep-spatio-temporal-residual-networks-for","repo_url":"https://github.com/uctb/uctb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"crowd-flows-prediction","task_name":"Crowd Flows Prediction"},{"task_slug":"management","task_name":"Management"}],"methods":[],"datasets_introduced":[{"slug":"taxibj","name":"TaxiBJ","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1610.00081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1610.00081"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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