{"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-multi-view-spatial-temporal-network-for","title":"Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction","arxiv_id":"1802.08714","date":"2018-02-23","proceeding":null,"authors":["Huaxiu Yao","Fei Wu","Jintao Ke","Xianfeng Tang","Yitian Jia","Siyu Lu","Pinghua Gong","Jieping Ye","Zhenhui Li"],"abstract":"Taxi demand prediction is an important building block to enabling intelligent\ntransportation systems in a smart city. An accurate prediction model can help\nthe city pre-allocate resources to meet travel demand and to reduce empty taxis\non streets which waste energy and worsen the traffic congestion. With the\nincreasing popularity of taxi requesting services such as Uber and Didi Chuxing\n(in China), we are able to collect large-scale taxi demand data continuously.\nHow to utilize such big data to improve the demand prediction is an interesting\nand critical real-world problem. Traditional demand prediction methods mostly\nrely on time series forecasting techniques, which fail to model the complex\nnon-linear spatial and temporal relations. Recent advances in deep learning\nhave shown superior performance on traditionally challenging tasks such as\nimage classification by learning the complex features and correlations from\nlarge-scale data. This breakthrough has inspired researchers to explore deep\nlearning techniques on traffic prediction problems. However, existing methods\non traffic prediction have only considered spatial relation (e.g., using CNN)\nor temporal relation (e.g., using LSTM) independently. We propose a Deep\nMulti-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial\nand temporal relations. Specifically, our proposed model consists of three\nviews: temporal view (modeling correlations between future demand values with\nnear time points via LSTM), spatial view (modeling local spatial correlation\nvia local CNN), and semantic view (modeling correlations among regions sharing\nsimilar temporal patterns). Experiments on large-scale real taxi demand data\ndemonstrate effectiveness of our approach over state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1802.08714v2","url_pdf":"http://arxiv.org/pdf/1802.08714v2.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-multi-view-spatial-temporal-network-for","repo_url":"https://github.com/huaxiuyao/DMVST-Net","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"},{"task_slug":"traffic-prediction","task_name":"Traffic Prediction"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.08714","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.08714"}},"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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