Passenger Flow Prediction Method based on Hybrid Algorithm: Intelligent Transportation System

Abstract

Forecasting passenger flow at metro transit stations is a useful method for optimizing the organization of passenger transportation and enhancing operational safety and transportation efficiency. Aiming at the problem that the traditional ARIMA model has poor performance in predicting passenger flow, a hybrid prediction method based on ARIMA-Kalman filtering is proposed. In this regard, ARIMA model training experimental samples are integrated with Kalman filter to create a prediction recursion equation, which is then utilized to estimate passenger flow. The simulation experiment results based on the inbound passenger flow data of Nanjing metro station show that compared with the single ARIMA model, the root mean square error of the prediction results of the proposed ARIMA-Kalman filter hybrid algorithm is reduced by 257.106, and the mean absolute error decreased by 145. 675, the mean absolute percentage error dropped by 5. 655%, proving that the proposed hybrid algorithm has higher prediction accuracy. The experiment results based on the passenger flow data of Nanjing metro station show that compared to a single ARIMA model, the proposed ARIMA Kalman filtering hybrid algorithm reduces the root mean square error of the prediction results by 257.106, the average absolute error by 145.675, and the average absolute percentage error by 5.655%. It has been proven that the proposed hybrid algorithm has higher prediction accuracy.