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Three Novel Methods to Predict Traffic Time Series in Reconstructed State Spaces

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  • Lawrence W. Lan

    (MingDao University, Taiwan)

  • Feng-Yu Lin

    (Central Police University, Taiwan)

  • April Y. Kuo

    (BNSF Railway, USA)

Abstract

This article proposes three novel methods—temporal confined (TC), spatiotemporal confined (STC) and spatial confined (SC)—to forecast the temporal evolution of traffic parameters. The fundamental rationales are to embed one-dimensional traffic time series into reconstructed state spaces and then to perform fuzzy reasoning to infer the future changes in traffic series. The TC, STC and SC methods respectively employ different fuzzy reasoning logics to select similar historical traffic trajectories. Theil inequality coefficient and its decomposed components are used to evaluate the predicting power and source of errors. Field observed one-minute traffic counts are used to test the predicting power. The results show that overall prediction accuracies for the three methods are satisfactorily high with small systematic errors and little deviation from the observed data. It suggests that the proposed three methods can be used to capture and forecast the short-term (e.g., one-minute) temporal evolution of traffic parameters.

Suggested Citation

  • Lawrence W. Lan & Feng-Yu Lin & April Y. Kuo, 2010. "Three Novel Methods to Predict Traffic Time Series in Reconstructed State Spaces," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 1(1), pages 16-35, January.
  • Handle: RePEc:igg:jaec00:v:1:y:2010:i:1:p:16-35
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    Cited by:

    1. Unsok Ryu & Jian Wang & Unjin Pak & Sonil Kwak & Kwangchol Ri & Junhyok Jang & Kyongjin Sok, 2022. "A clustering based traffic flow prediction method with dynamic spatiotemporal correlation analysis," Transportation, Springer, vol. 49(3), pages 951-988, June.

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