Dynamic public transport passenger flow forecast based on IMM method

Zhenliang Ma, Jianping Xing, Liang Gao, Junchen Sha, Yong Wu, Yubing Wu

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Multimedia, Software Engineering and Computing Vol.2
Subtitle of host publicationProceedings of the 2011 MSEC International Conference on Multimedia, Software Engineering and Computing, November 26-27, Wuhan, China
EditorsDavid Jin, Sally Lin
Pages675-683
Number of pages9
DOIs
Publication statusPublished - 1 Dec 2011

Publication series

NameAdvances in Intelligent and Soft Computing
Volume129
ISSN (Print)1867-5662

Keywords

  • DA
  • IMM
  • Urban public transport passenger flow forecast

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