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+is+considered+essential.+However%252C+the+existing+road-level+traffic+prediction+has+mainly+studied+how+significantly+micro+traffic+events+propagate+to+the+adjacent+roads+in+terms+of+short-term+prediction.+On+the+other+hand%252C+recent+attempts+have+been+made+to+incorporate+regional+knowledge+such+as+POIs%252C+road+characteristics%252C+and+real-time+social+events+to+help+traffic+prediction.+However%252C+these+studies+lack+in+understandings+of+different+modalities+of+road-level+and+region-level+spatio-temporal+correlations+and+how+to+combine+such+knowledge.+This+paper+proposes+a+novel+method+that+embeds+real-time+region-level+knowledge+using+POIs%252C+satellite+images%252C+and+real-time+LTE+access+traces+via+a+regional+spatio-temporal+module+that+consists+of+dynamic+convolution+and+temporal+attention%252C+and+conducts+bipartite+spatial+transform+attention+to+convert+into+road-level+knowledge.+Then+the+model+ingests+this+embedded+knowledge+into+a+road-level+attention-based+prediction+model.+Experimental+results+on+real-world+road+traffic+prediction+show+that+our+model+outperforms+the+baselines.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.37f3b492a42e672dcbd300e8b24b12db%3frik%3dg48rAJOVyNJsaQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Temporal+Resolution+Traffic&FORM=IRPRST&ck=35ECD5167F5CF03C0AC8F17DDAC32F55&selectedIndex=11&itb=0)
