Prophet-DCRNN traffic flow prediction method fusing multi-modal information
The invention discloses a Prophet-DCRNN traffic flow prediction method fusing multi-modal information, and belongs to the technical field of traffic flow prediction. Although an existing flow prediction method based on deep learning well captures time-space characteristics of traffic flow, actual ur...
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| creator | XU YANG SONG KAILEI HOU WEIZHAO CHEN XIAODONG HAN ZHIZHUO JIN YABIN SU HUIJIE ZHAN KETONG ZHANG ZHITAO ZANG YANJUN |
| description | The invention discloses a Prophet-DCRNN traffic flow prediction method fusing multi-modal information, and belongs to the technical field of traffic flow prediction. Although an existing flow prediction method based on deep learning well captures time-space characteristics of traffic flow, actual urban traffic is affected by factors such as weather, holidays and festivals, and meanwhile, traffic jam tends to occur in severe weather, holidays and festivals. The provides the Prophet-DCRNN traffic flow prediction method fusing multi-modal information to overcome the defects of the prior art. The method uses a Prophet time sequence prediction algorithm to capture holiday effects, uses a DCRNN to capture traffic space-time characteristics, and in addition, based on a stacking-like technology, the Prophet algorithm, the DCRNN algorithm, holiday characteristics and weather information are fused, a hybrid model that finally fuses multi-modal information is obtained, so the accuracy of traffic prediction in festivals |
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Although an existing flow prediction method based on deep learning well captures time-space characteristics of traffic flow, actual urban traffic is affected by factors such as weather, holidays and festivals, and meanwhile, traffic jam tends to occur in severe weather, holidays and festivals. The provides the Prophet-DCRNN traffic flow prediction method fusing multi-modal information to overcome the defects of the prior art. The method uses a Prophet time sequence prediction algorithm to capture holiday effects, uses a DCRNN to capture traffic space-time characteristics, and in addition, based on a stacking-like technology, the Prophet algorithm, the DCRNN algorithm, holiday characteristics and weather information are fused, a hybrid model that finally fuses multi-modal information is obtained, so the accuracy of traffic prediction in festivals</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; PHYSICS ; SIGNALLING ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR ; TRAFFIC CONTROL SYSTEMS</subject><creationdate>2021</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20210702&DB=EPODOC&CC=CN&NR=113066288A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20210702&DB=EPODOC&CC=CN&NR=113066288A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>XU YANG</creatorcontrib><creatorcontrib>SONG KAILEI</creatorcontrib><creatorcontrib>HOU WEIZHAO</creatorcontrib><creatorcontrib>CHEN XIAODONG</creatorcontrib><creatorcontrib>HAN ZHIZHUO</creatorcontrib><creatorcontrib>JIN YABIN</creatorcontrib><creatorcontrib>SU HUIJIE</creatorcontrib><creatorcontrib>ZHAN KETONG</creatorcontrib><creatorcontrib>ZHANG ZHITAO</creatorcontrib><creatorcontrib>ZANG YANJUN</creatorcontrib><title>Prophet-DCRNN traffic flow prediction method fusing multi-modal information</title><description>The invention discloses a Prophet-DCRNN traffic flow prediction method fusing multi-modal information, and belongs to the technical field of traffic flow prediction. 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Although an existing flow prediction method based on deep learning well captures time-space characteristics of traffic flow, actual urban traffic is affected by factors such as weather, holidays and festivals, and meanwhile, traffic jam tends to occur in severe weather, holidays and festivals. The provides the Prophet-DCRNN traffic flow prediction method fusing multi-modal information to overcome the defects of the prior art. The method uses a Prophet time sequence prediction algorithm to capture holiday effects, uses a DCRNN to capture traffic space-time characteristics, and in addition, based on a stacking-like technology, the Prophet algorithm, the DCRNN algorithm, holiday characteristics and weather information are fused, a hybrid model that finally fuses multi-modal information is obtained, so the accuracy of traffic prediction in festivals</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES PHYSICS SIGNALLING SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR TRAFFIC CONTROL SYSTEMS |
| title | Prophet-DCRNN traffic flow prediction method fusing multi-modal information |
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