Methods for Selecting Temporal Granularity in Passenger Flow Forecasting

With the increasing improvement of urban rail transit network, the passenger flow of rail transit is also increasing year by year, and the management methods for passenger flow are becoming more and more intelligent and informative. To grasp the changing pattern of passenger flow has become the focus of research on rail transit operation. Previous studies have been conducted in a single time granularity for prediction, ignoring the influence of different time granularities on the prediction results. Furthermore, selecting time granularity solely based on prediction accuracy is inadequate, as larger time granularities result in a loss of detail regarding the specific changes in passenger flow, which can be referred to as passenger flow characteristics. In order to address the above issues, this study use Person correlation analysis and systematic clustering method to select the best time granularity. Then the BP neural network is employed to verify the prediction effect of different time granularity. The experimental results prove that the time granularity selection method proposed in this study can effectively improve the prediction accuracy and the expression effect of passenger flow characteristics.