Lane-level traffic flow prediction method based on dynamic traffic flow big data
The invention belongs to the field of traffic flow prediction, and discloses a lane-level traffic flow prediction method based on dynamic traffic flow big data, and the method comprises the steps: S1, collecting the historical traffic flow information of a to-be-predicted road network; s2, construct...
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| creator | LUO ZHIWEI CHEN MINJIE TANG JIEXIA LIU JUNJIE WANG GUICHENG LUO ZHENQUAN LIU WEILIN CHEN XUGAO LIANG GUOXIONG LIU HAIXIA LI LINMAO LI ZIJUN YE KUNHAN QIN WEN CHEN ZHIPING XIAO GUOMEI |
| description | The invention belongs to the field of traffic flow prediction, and discloses a lane-level traffic flow prediction method based on dynamic traffic flow big data, and the method comprises the steps: S1, collecting the historical traffic flow information of a to-be-predicted road network; s2, constructing a road network historical traffic flow information matrix, and processing the road network historical traffic flow information matrix by using expansion convolution; constructing the traffic flow information of the same node in the road network at different historical times into vectors, and calculating a learnable matrix based on the vectors of all nodes; s3, constructing a spatial feature adjacency matrix based on data driving; and S4, a gating structure is designed to integrate the time features and the space features, and the gating structure is designed based on an attention mechanism and global pooling operation to obtain a final prediction result. According to the method, the time features are extracted |
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s2, constructing a road network historical traffic flow information matrix, and processing the road network historical traffic flow information matrix by using expansion convolution; constructing the traffic flow information of the same node in the road network at different historical times into vectors, and calculating a learnable matrix based on the vectors of all nodes; s3, constructing a spatial feature adjacency matrix based on data driving; and S4, a gating structure is designed to integrate the time features and the space features, and the gating structure is designed based on an attention mechanism and global pooling operation to obtain a final prediction result. According to the method, the time features are extracted</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 | Lane-level traffic flow prediction method based on dynamic traffic flow big data |
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