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Article

Short-Term Bus Passenger Flow Prediction Based on Graph Diffusion Convolutional Recurrent Neural Network

Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
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Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(8), 4910; https://doi.org/10.3390/app13084910
Submission received: 21 March 2023 / Revised: 9 April 2023 / Accepted: 11 April 2023 / Published: 13 April 2023

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This study integrates diffusion convolution in a graph into a recurrent neural network to capture the spatiotemporal dependencies of different bus lines in a bus network for better passenger flow prediction. The proposed method is implemented in the bus network of Jiading, Shanghai, and achieves better modeling performance than that of the classic recurrent neural network models.

Abstract

The short-term bus passenger flow prediction of each bus line in a transit network is the basis of real-time cross-line bus dispatching, which ensures the efficient utilization of bus vehicle resources. As bus passengers transfer between different lines, to increase the accuracy of prediction, we integrate graph features into the recurrent neural network (RNN) to capture the spatiotemporal dependencies in the bus network. The diffusion convolution recurrent neural network (DCRNN) architecture is adopted to forecast the future number of passengers on each bus line. The demand evolution in the bus network of Jiading, Shanghai, is investigated to demonstrate the effectiveness of the DCRNN model. Compared with classic RNN models, our proposed method has an advantage of about 5% in mean average percentage error (MAPE). The incorporation of diffusion convolution shows that the travel demand in a bus line tends to be similar to that in the closely related lines. In addition, the improvement in MAPE shows that this model outputs more accurate prediction values for low-demand bus lines. It ensures that, for real-time cross-line bus dispatching with limited vehicle resources, the low-demand bus lines are less likely to be affected to maintain a decent level of service of the whole bus network.
Keywords: bus transit; passenger flow prediction; spatiotemporal dependencies; graph convolution; recurrent neural network bus transit; passenger flow prediction; spatiotemporal dependencies; graph convolution; recurrent neural network

Share and Cite

MDPI and ACS Style

Zhai, X.; Shen, Y. Short-Term Bus Passenger Flow Prediction Based on Graph Diffusion Convolutional Recurrent Neural Network. Appl. Sci. 2023, 13, 4910. https://doi.org/10.3390/app13084910

AMA Style

Zhai X, Shen Y. Short-Term Bus Passenger Flow Prediction Based on Graph Diffusion Convolutional Recurrent Neural Network. Applied Sciences. 2023; 13(8):4910. https://doi.org/10.3390/app13084910

Chicago/Turabian Style

Zhai, Xubin, and Yu Shen. 2023. "Short-Term Bus Passenger Flow Prediction Based on Graph Diffusion Convolutional Recurrent Neural Network" Applied Sciences 13, no. 8: 4910. https://doi.org/10.3390/app13084910

APA Style

Zhai, X., & Shen, Y. (2023). Short-Term Bus Passenger Flow Prediction Based on Graph Diffusion Convolutional Recurrent Neural Network. Applied Sciences, 13(8), 4910. https://doi.org/10.3390/app13084910

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