Traffic Flow Prediction Model Based on Drivers’ Cognition of Road Network
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- Li Songjiang
- School of Computer Science and Technology, Changchun University of Science and Technology
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- An Wen
- School of Computer Science and Technology, Changchun University of Science and Technology
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- Wang Peng
- School of Computer Science and Technology, Changchun University of Science and Technology
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<p>The traditional traffic flow prediction method is based on data modeling, when emergencies occur, it is impossible to accurately analyze the changes in traffic characteristics. This paper proposes a traffic flow prediction model (BAT-GCN) which is based on drivers’ cognition of the road network. Firstly, drivers can judge the capacity of different paths by analyzing the travel time in the road network, which bases on the drivers’ cognition of road network space. Secondly, under the condition that the known road information is obtained, people through game decision-making for different road sections to establish the probability model of path selection; Finally, drivers obtain the probability distribution of different paths in the regional road network and build the prediction model by combining the spatiotemporal directed graph convolution neural network. The experimental results show that the BAT-GCN model reduces the prediction error compared with other baseline models in the peak period.</p>
収録刊行物
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- Journal of Advanced Computational Intelligence and Intelligent Informatics
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Journal of Advanced Computational Intelligence and Intelligent Informatics 24 (7), 900-907, 2020-12-20
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詳細情報 詳細情報について
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- CRID
- 1390286981361089024
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- NII論文ID
- 130007958516
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- NII書誌ID
- AA12042502
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- ISSN
- 18838014
- 13430130
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- NDL書誌ID
- 031195702
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
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- CiNii Articles
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- 使用不可