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- W4365514314 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." @default.
- W4365514314 created "2023-04-15" @default.
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- W4365514314 date "2023-04-13" @default.
- W4365514314 modified "2023-10-14" @default.
- W4365514314 title "Short-Term Bus Passenger Flow Prediction Based on Graph Diffusion Convolutional Recurrent Neural Network" @default.
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- W4365514314 doi "https://doi.org/10.3390/app13084910" @default.
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