Dynamic Public Transport Passenger Flow Forecast Based on IMM Method

In this paper, an dynamic urban public transport passenger flow forecasting approach is proposed based on interact multiple model (IMM) method. The dynamic approach (DA) maximizes useful information content by assembling knowledge from correlate time sequences, and making full use of historical and real-time passenger flow data. The dynamic approach is accomplished as follows: By analyzing the source data, three correlate times sequences are constructed. The auto-regression (AR), autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA) models are selected to give predictions of the three correlate time sequence. The output of the dynamic IMM serves as the final prediction using the results from the three models. To assess the performance of different approaches, moving average, exponential smoothing, artificial neural network, ARIMA and the proposed dynamic approach are applied to the real passenger flow prediction. The results suggest that the DA can obtain a more accurate prediction than the other approaches.

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