Kalman filter algorithm for short-term jam traffic prediction based on traffic parameter correlation

Chunjiao Dong, Chunfu Shao, Xuemei Zhou, Meng Meng, Chengxiang Zhuge

Research output: Contribution to journalArticlepeer-review

10 Citations (SciVal)

Abstract

A Kalman filter model considering the correlation property of traffic flow parameters is proposed to realize network short-term traffic flow prediction under jam traffic. The proposed state-space model of short-term traffic flow prediction is presented by solving the conservation equation using Lax-Wendroff scheme. In addition, the spatial-temporal characteristics of the traffic flow on urban expressway networks and the influence factors of on and off ramp are taken into account for flow rate prediction. The estimation algorithm of the proposed state-space model is designed based on the Kalman filter method. A region expressway network in Beijing is taken as an example to evaluate the performance of the proposed method. The results show that the maximum prediction mean absolute percentage error(MAPE) of the proposed Kalman filter model is less than 10% since the input of the Kalman filter model considers the impacts of spatial-temporal characteristics, and the mean of prediction MAPE is 7.96%. For the same predicted conditions, the mean prediction MAPEs of ARIMA and Elman model are 19.88% and 10.51%, respectively.

Original languageEnglish
Pages (from-to)413-419
Number of pages7
JournalDongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition)
Volume44
Issue number2
DOIs
Publication statusPublished - 2014

Keywords

  • Jam traffic
  • Kalman filter
  • Short-term traffic flow prediction
  • State-space model

ASJC Scopus subject areas

  • Engineering(all)

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