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

Lawrence W. Lan, Feng-Yu Lin and April Y. Kuo
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Lawrence W. Lan: MingDao University, Taiwan
Feng-Yu Lin: Central Police University, Taiwan
April Y. Kuo: BNSF Railway, USA

International Journal of Applied Evolutionary Computation (IJAEC), 2010, vol. 1, issue 1, 16-35

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.

Date: 2010
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