Dynamic berth allocation method based on deep learning and objective weighting method
The invention discloses a parking space dynamic allocation method based on deep learning and an objective weighting method, and belongs to the field of computer vision and intelligent parking. On the basis of a first arrival first arrival parking mode, two reservation parking modes, namely, reservat...
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| creator | XU WENKAI MAO XINBO PAN FAHUI LIU SHUO GAO GE CHEN RONG |
| description | The invention discloses a parking space dynamic allocation method based on deep learning and an objective weighting method, and belongs to the field of computer vision and intelligent parking. On the basis of a first arrival first arrival parking mode, two reservation parking modes, namely, reservation first serving and auction reservation, are introduced, and two parameters are set; the parking berth proportion is a parking berth proportion # imgabs0 # for first arrival and first arrival parking and a parking berth proportion # imgabs1 # for first reservation and first service; according to the invention, the deep neural network and the CRITIC objective weighting method are used to realize the comprehensive decision-making and dynamic allocation of the parking space ratio in the hybrid parking mode, the parking resource configuration is optimized, the parking efficiency and convenience are improved, and a new thought is provided for the practical application of different parking reservation strategies.
本发明公开 |
| format | Patent |
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本发明公开</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><subject>SIGNALLING</subject><subject>TRAFFIC CONTROL SYSTEMS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNi0EKwjAUBbNxIeodvgcQLFHoVqriypWuy0_ybCJpEpqgeHsregBXwxveTMV1_wrcO00KQ7HE3kfNxcVAPYqNhhRnGBq3ARJ58BBc6IjDKNUdurgH6AnX2fLx32ouJjf2GYsfZ2J5PFya0woptsiJNQJK25yrqt7Kei03O_nP5w3Lfjke</recordid><startdate>20240823</startdate><enddate>20240823</enddate><creator>XU WENKAI</creator><creator>MAO XINBO</creator><creator>PAN FAHUI</creator><creator>LIU SHUO</creator><creator>GAO GE</creator><creator>CHEN RONG</creator><scope>EVB</scope></search><sort><creationdate>20240823</creationdate><title>Dynamic berth allocation method based on deep learning and objective weighting method</title><author>XU WENKAI ; MAO XINBO ; PAN FAHUI ; LIU SHUO ; GAO GE ; CHEN RONG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118538034A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><topic>SIGNALLING</topic><topic>TRAFFIC CONTROL SYSTEMS</topic><toplevel>online_resources</toplevel><creatorcontrib>XU WENKAI</creatorcontrib><creatorcontrib>MAO XINBO</creatorcontrib><creatorcontrib>PAN FAHUI</creatorcontrib><creatorcontrib>LIU SHUO</creatorcontrib><creatorcontrib>GAO GE</creatorcontrib><creatorcontrib>CHEN RONG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>XU WENKAI</au><au>MAO XINBO</au><au>PAN FAHUI</au><au>LIU SHUO</au><au>GAO GE</au><au>CHEN RONG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Dynamic berth allocation method based on deep learning and objective weighting method</title><date>2024-08-23</date><risdate>2024</risdate><abstract>The invention discloses a parking space dynamic allocation method based on deep learning and an objective weighting method, and belongs to the field of computer vision and intelligent parking. On the basis of a first arrival first arrival parking mode, two reservation parking modes, namely, reservation first serving and auction reservation, are introduced, and two parameters are set; the parking berth proportion is a parking berth proportion # imgabs0 # for first arrival and first arrival parking and a parking berth proportion # imgabs1 # for first reservation and first service; according to the invention, the deep neural network and the CRITIC objective weighting method are used to realize the comprehensive decision-making and dynamic allocation of the parking space ratio in the hybrid parking mode, the parking resource configuration is optimized, the parking efficiency and convenience are improved, and a new thought is provided for the practical application of different parking reservation strategies.
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS SIGNALLING TRAFFIC CONTROL SYSTEMS |
| title | Dynamic berth allocation method based on deep learning and objective weighting method |
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