基于注意力机制和分时图卷积的公交客流预测

doi:10.16451/j.cnki.issn1003-6059.202102008

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Abstract 1cvarying system. Therefore, the spatiotemporal correlation between different bus lines can hardly be built effectively. To solve this problem, an attention and time-sharing graph convolution based long short-term memory network for bus passenger flow forecast is proposed. Firstly, temporal features of historical data are extracted by long short-term memory network(LSTM), and then they are weighted by a channel-wise attention module. A time-sharing graph convolution approach is utilized to analyze the spatial dependencies among bus lines. Different adjacent matrices are selected according to time intervals, and non-Euclidean pair-wise correlations are modeled via graph convolution. Finally, the final prediction result is obtained by integrating the extracted spatiotemporal features and vector representations of external factors, like weather and holiday information. Experiments on real bus passenger flow datasets indicate that the proposed model improves the prediction accuracy and learning speed evidently.

Key words： Intelligent Transportation Bus Passenger Flow Prediction Recurrent Neural Network Channel-Wise Attention Time-Sharing Graph Convolution

Received: 17 August 2020

ZTFLH:

TP 391.4

Corresponding Authors: ZHU Fenghua, Ph.D., associate professor. His research interests include artificial transportation system, parallel transportation system and intelligent control.

About author:: ZHANG Wei, master student. His research interests include traffic data mining and inte-lligent transportation system. CHEN Yuanyuan, Ph.D., assistant professor. His research interests include social transportation system, and data-driven traffic modeling and prediction. LÜ Yisheng, Ph.D., associate professor. His research interests include intelligent transportation system, artificial intelligence, and parallel traffic management and control system.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202102008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I2/167

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