{"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/deeptfp-mobile-time-series-data-analytics","title":"DeepTFP: Mobile Time Series Data Analytics based Traffic Flow Prediction","arxiv_id":"1710.01695","date":"2017-10-01","proceeding":null,"authors":["Yuanfang Chen","Falin Chen","Yizhi Ren","Ting Wu","Ye Yao"],"abstract":"Traffic flow prediction is an important research issue to avoid traffic\ncongestion in transportation systems. Traffic congestion avoiding can be\nachieved by knowing traffic flow and then conducting transportation planning.\nAchieving traffic flow prediction is challenging as the prediction is affected\nby many complex factors such as inter-region traffic, vehicles' relations, and\nsudden events. However, as the mobile data of vehicles has been widely\ncollected by sensor-embedded devices in transportation systems, it is possible\nto predict the traffic flow by analysing mobile data. This study proposes a\ndeep learning based prediction algorithm, DeepTFP, to collectively predict the\ntraffic flow on each and every traffic road of a city. This algorithm uses\nthree deep residual neural networks to model temporal closeness, period, and\ntrend properties of traffic flow. Each residual neural network consists of a\nbranch of residual convolutional units. DeepTFP aggregates the outputs of the\nthree residual neural networks to optimize the parameters of a time series\nprediction model. Contrast experiments on mobile time series data from the\ntransportation system of England demonstrate that the proposed DeepTFP\noutperforms the Long Short-Term Memory (LSTM) architecture based method in\nprediction accuracy.","url_abs":"http://arxiv.org/abs/1710.01695v1","url_pdf":"http://arxiv.org/pdf/1710.01695v1.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":"deeptfp-mobile-time-series-data-analytics","repo_url":"https://github.com/tbinetruy/CIL4SYS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-prediction","task_name":"Time Series Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}