In order to effectively alleviate daily traffic congestion, advocate healthy and green travel, and
accurately grasp the distribution characteristics of passenger flow in urban traffic through the analysis and
prediction of urban passenger flow characteristics, this paper comprehensively considers the influence of
weather, holidays and other factors on passenger flow. The product neural network proposes a causal
convolutional self-attention urban bus passenger flow, which improves the local information extraction ability
of the input sequence and obtains long-term dependency information, thereby improving the prediction
accuracy. In the experiment, causal convolutional self-attention is used for dependency feature extraction, the
sample calculation cost of the encoder is controlled by the CCN-Informer model, and the generative structure
decoder is used to improve the model prediction efficiency. The results show that the CCN-Informer model
considers the influence of the surrounding important characteristic factors, and the fitting degree of the sample
data is significantly improved. Compared with the baseline model, the standard error decreases between 4%
and 20%, and it has higher prediction accuracy and operation efficiency. The validity of the model is verified,
and it can provide a basis for the prediction of urban traffic passenger flow.
LIU Miaonan, WANG Wei, HU Xianhui, XU Dehao.
Urban Bus Passenger Flow Forecasting Based on Causal Convolution and
Informer Model[J]. Control Engineering of China, 2024, 31(8): 1445-1454