Short-Term Forecasting of Passenger Flow on the Metro Platform Using an Improved Kalman Filtering Method

The short-term forecasting of passenger flow on the metro platform is the decision-making basis and technical support for the operation and management of metro. In this paper, we developed an improved Kalman filter model to forecast short-term (15 min) passenger fluctuations after analyzing the characteristic of metro platform. The model illustration was conducted on the island, side, regular, and transfer metro platform in Beijing, respectively. Compared with the traditional Kalman filter model, the results showed that the average absolute error of the model was 0.299, the mean square error was 34.094, and the equal coefficient was 0.923, indicating that the proposed model could effectively predict the short-term passenger on the metro platform. Compared with the traditional Kalman filter method, the model presented in this paper can improve the real-time prediction accuracy and reduce the average absolute error by 0.448. These insights will help build more prosperous and sustainable metro systems.