Application of Improved Algorithm Based on Four-Dimensional ResNet in Rural Tourism Passenger Flow Prediction

In rural tourism, precise visitor flow forecasts may aid management in making better decisions. It aids in the reduction of visitor crowds and trash. It also has the potential to improve visitor security. As a result, it is critical to continue to encourage tourism's long-term growth. However, regional tourist flow of rural tourism has the characteristics of high volatility, complex nonlinearity, and susceptibility to seasonal influences. Moreover, a single neural network model cannot learn both temporal and spatial correlation. Therefore, by examining the variables impacting regional tourist flow and integrating residual networks with fully linked networks, this research offers an enhanced Quad-ResNet model for forecasting regional tourist flow of rural tourism. To be specific, this model learns spatial correlation through deep convolution; combines four residual networks to learn temporal proximity, similarity, periodicity and tendency; and uses one fully connected network to learn seasonal effects. Furthermore, this study compares the Quad-ResNet model with LSTM, CNN, and ST-ResNet models on the same dataset for regional tourist flow prediction experiments. The findings reveal that the Quad-ResNet model has less error and is substantially simpler to train and predict than the LSTM model, making it more suited for predicting regional visitor flows in rural tourism. For relevant stakeholders, the generated model may serve as a useful decision-making tool.