Kalman Filter-Based Short-Term Passenger Flow Forecasting on Bus Stop

Short-term passenger flow forecasting on bus stop is an important base and technical support for decision-making on the intelligent bus dispatch system.A Kalman filter method is developed to forecast short-term passenger flow based on the characteristic analysis,and the solving algorism is presented.In the real bus line,a typical bus station with large and significantly changed passenger flow is chosen to be an example.The prediction method and the artificial neural network had been compared with the results,which show that the Kalman filter-based model is more accurate.The calculated average absolute error is 5.177 1 and mean square error is 0.796 1 using the Kalman filter and the average absolute error is 10.477 0 and mean square error is 1.672 4 using the artificial neural network,which indicates that the prediction error is small,and the method is meaningful in practical application.