Scenic area passenger flow real-time prediction method based on tourist time-space behavior pattern mining
The invention provides a scenic spot passenger flow real-time prediction method based on tourist time-space behavior pattern mining. The scenic spot passenger flow real-time prediction method comprises the following steps: S1, real-time collection of big data: carrying out real-time collection on ba...
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creator | WANG JIANYING YU HAITAO TAN LIGUO WU ZHONGJUN HE JIANWU YAO JIANSHENG ZHOU MAOJIE |
description | The invention provides a scenic spot passenger flow real-time prediction method based on tourist time-space behavior pattern mining. The scenic spot passenger flow real-time prediction method comprises the following steps: S1, real-time collection of big data: carrying out real-time collection on basic information, time-space behavior data, traffic condition data, meteorological data and destination festival data of tourists; s2, mining a space-time behavior mode of tourists and constructing a space reachability model between scenic spots; s3, predicting the passenger flow quantity of each scenic spot in the next prediction period by using the model constructed in the step S2 according to the spatial and temporal distribution of tourists in each scenic spot at the current moment of the destination, tourist movement directivity information and traffic condition data; and S4, performing iterative updating on the model constructed in the step S2 according to tourist spatio-temporal data generated in real time in |
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The scenic spot passenger flow real-time prediction method comprises the following steps: S1, real-time collection of big data: carrying out real-time collection on basic information, time-space behavior data, traffic condition data, meteorological data and destination festival data of tourists; s2, mining a space-time behavior mode of tourists and constructing a space reachability model between scenic spots; s3, predicting the passenger flow quantity of each scenic spot in the next prediction period by using the model constructed in the step S2 according to the spatial and temporal distribution of tourists in each scenic spot at the current moment of the destination, tourist movement directivity information and traffic condition data; and S4, performing iterative updating on the model constructed in the step S2 according to tourist spatio-temporal data generated in real time in</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 ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2022</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=20220520&DB=EPODOC&CC=CN&NR=114519454A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220520&DB=EPODOC&CC=CN&NR=114519454A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>WANG JIANYING</creatorcontrib><creatorcontrib>YU HAITAO</creatorcontrib><creatorcontrib>TAN LIGUO</creatorcontrib><creatorcontrib>WU ZHONGJUN</creatorcontrib><creatorcontrib>HE JIANWU</creatorcontrib><creatorcontrib>YAO JIANSHENG</creatorcontrib><creatorcontrib>ZHOU MAOJIE</creatorcontrib><title>Scenic area passenger flow real-time prediction method based on tourist time-space behavior pattern mining</title><description>The invention provides a scenic spot passenger flow real-time prediction method based on tourist time-space behavior pattern mining. 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The scenic spot passenger flow real-time prediction method comprises the following steps: S1, real-time collection of big data: carrying out real-time collection on basic information, time-space behavior data, traffic condition data, meteorological data and destination festival data of tourists; s2, mining a space-time behavior mode of tourists and constructing a space reachability model between scenic spots; s3, predicting the passenger flow quantity of each scenic spot in the next prediction period by using the model constructed in the step S2 according to the spatial and temporal distribution of tourists in each scenic spot at the current moment of the destination, tourist movement directivity information and traffic condition data; and S4, performing iterative updating on the model constructed in the step S2 according to tourist spatio-temporal data generated in real time in</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 ELECTRIC DIGITAL DATA PROCESSING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
title | Scenic area passenger flow real-time prediction method based on tourist time-space behavior pattern mining |
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