Adaptive and Dynamic Spatio-Temporal Network for Traffic Flow Forecasting

Adaptive and Dynamic Spatio-Temporal Network for Traffic Flow Forecasting | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Software: Practice and Experience This is a preprint and has not been peer reviewed. Data may be preliminary. 2 July 2025 V1 Latest version Share on Adaptive and Dynamic Spatio-Temporal Network for Traffic Flow Forecasting Authors : Ying Xing , Bin Yang , Tianyu Lu 0009-0008-1743-4530 , Yun Yang [email protected] , Weiwei Jiang 0000-0003-0953-5047 , Ligang Ren , and Jinhua Liang Authors Info & Affiliations https://doi.org/10.22541/au.175149195.57988118/v1 Published Software: Practice and Experience Version of record Peer review timeline 248 views 201 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Urban transportation systems are essential in fulfilling the requirements of residents while guaranteeing the normal functioning of cities. These systems encompass various modes of transportation, infrastructure, and services that enable people to move within and between urban areas. However, the challenges posed by escalating urbanization, particularly the growing menace of traffic congestion, underscore the pressing need for effective solutions. We propose an Adaptive and Dynamic Spatio-Temporal Network (ADSTN) as an innovative solution to the complexities associated with traffic congestion. The identified shortcomings in existing models, such as their limitations in capturing authentic spatial dependencies, insufficient understanding of the heterogeneous relationship between the temporal and spatial domains, and the oversight of local trend information, motivate the development of ADSTN. The model integrates three key components: a learnable adaptive attention module, a local temporal self-attention block, and a spatio-temporal dynamic graph convolution block. ADSTN stands out for its outstanding performance in handling local spatio-temporal dependencies, periodicity, and dynamics within traffic flow forecasting. Evaluation on three public real-world datasets underscores competitive achievements of ADSTN contrasted to state-of-the-art models, all while maintaining computational efficiency. Supplementary Material File (adaptive and dynamic spatio-temporal network for traffic flow forecasting.pdf) Download 1.58 MB Information & Authors Information Version history V1 Version 1 02 July 2025 Peer review timeline Published Software: Practice and Experience Version of Record 5 Dec 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Software: Practice and Experience Keywords adaptive attention dynamic graph convolution local temporal self-attention traffic flow forecasting transportation system Authors Affiliations Ying Xing Beijing University of Posts and Telecommunications View all articles by this author Bin Yang China United Network Communications Co Ltd View all articles by this author Tianyu Lu 0009-0008-1743-4530 Beijing University of Posts and Telecommunications View all articles by this author Yun Yang [email protected] Yunnan University View all articles by this author Weiwei Jiang 0000-0003-0953-5047 Beijing University of Posts and Telecommunications View all articles by this author Ligang Ren China United Network Communications Co Ltd View all articles by this author Jinhua Liang Beijing Jingneng information Technology Co Ltd View all articles by this author Metrics & Citations Metrics Article Usage 248 views 201 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ying Xing, Bin Yang, Tianyu Lu, et al. Adaptive and Dynamic Spatio-Temporal Network for Traffic Flow Forecasting. Authorea . 02 July 2025. DOI: https://doi.org/10.22541/au.175149195.57988118/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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