![]() Castro used a support vector machine model to predict traffic flow under typical and atypical traffic conditions and achieved better prediction results. The nonlinear models, such as nonlinear time series model, support vector machine model, and neural network model, are considered to have more accuracy in describing the characteristics of transit systems and better performance than linear models in passenger flow prediction. ![]() Xue used the linear time series model to predict the short-term passenger flow of public transport, and the results showed that the time series model has defects in predicting the short-term passenger flow and it is more suitable for predicting the long-term passenger flow. Linear time series model, historical average model, nearest neighbor model, and error component model are the kind of linear models which are used to infer the trend of passenger flow in some scenarios with specific theoretical assumptions. With linear models, the empirical data are mainly used to predict passenger flow under theoretical assumptions and specific condition parameters. In the area of passenger flow prediction for public transport, the mathematical prediction models that the researchers have used can be divided into linear models and nonlinear models, as far as we know. Passenger flow prediction is considered the foremost and pivotal technology in improving the management standard and service level of metro, as well as other public transport modes. Based on the data source from metro line 2 in Qingdao, China, the perdition results show the proposed methods have a good accuracy, with Mean Absolute Percentage Errors (MAPEs) of 11.6%, 3.24%, and 3.86 corresponding to the metro line prediction model with Categorical Regression (CATREG), single metro station prediction model with Artificial Neural Network (ANN), and single metro station prediction model with Long Short-Term Memory (LSTM), respectively. It aims to provide a feasible solution to predict the passenger flow based on land uses around the metro stations and then potentially improving the understanding of the land uses around the metro station impact on the metro passenger flow, and exploring the potential association between the land uses and the metro passenger flow. This paper uses mathematical and neural network modeling methods to predict metro passenger flow based on the land uses around the metro stations, along with considering the spatial correlation of metro stations within the metro line and the temporal correlation of time series in passenger flow prediction. It is an important technological means in ensuring sustainable and steady development of urban transportation. ![]() Passenger flow prediction is the foremost and pivotal technology in improving the management standard and service level of metro. It calls for traffic agents to reinforce the operation and management standard to improve the service level. However, the capacity of the metro cannot always meet the traffic demand during rush hours. ![]() Metro as an urban mass transit mode is considered as a sustainable strategy to balance the urban high-density land uses development and the high-intensity traffic demand. High-density land uses cause high-intensity traffic demand. ![]()
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