Prediction of Public Transit Passenger Volume of Beijing Based on Gray Neural Network

In recent years, public transportation has occupied the leading status among other modes of transport. The prediction of public transit passenger volume is directly related to the scientific basis and rationality of urban transit planning. In this paper, historical data and influencing factors of public transit passenger volume of Beijing are fully considered from five aspects (bus system, competitors, travel demand, urban construction development, and incidental events). After the implement of gray correlation degree, nine influencing indicators of greater correlation with public transit passenger volume are used as comparison sequences for subsequent prediction. Then based on the normalized data, this paper establishes a gray neural network to predict public transit passenger volume by MATLAB programming. Finally, the prediction accuracy of the gray neural network model is proved by comparing with the results of a single BP neural network model.