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Real‐time road traffic forecasting using regime‐switching space‐time models and adaptive LASSO

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  • Yiannis Kamarianakis
  • Wei Shen
  • Laura Wynter

Abstract

Smart transportation technologies require real‐time traffic prediction to be both fast and scalable to full urban networks. We discuss a method that is able to meet this challenge while accounting for nonlinear traffic dynamics and space‐time dependencies of traffic variables. Nonlinearity is taken into account by a union of non‐overlapping linear regimes characterized by a sequence of temporal thresholds. In each regime, for each measurement location, a penalized estimation scheme, namely the adaptive absolute shrinkage and selection operator (LASSO), is implemented to perform model selection and coefficient estimation simultaneously. Both the robust to outliers least absolute deviation estimates and conventional LASSO estimates are considered. The methodology is illustrated on 5‐minute average speed data from three highway networks. Copyright © 2012 John Wiley & Sons, Ltd.

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  • Yiannis Kamarianakis & Wei Shen & Laura Wynter, 2012. "Real‐time road traffic forecasting using regime‐switching space‐time models and adaptive LASSO," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 28(4), pages 297-315, July.
    • Handle: RePEc:wly:apsmbi:v:28:y:2012:i:4:p:297-315
    DOI: 10.1002/asmb.1937
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    Cited by:

    1. Hyun-Joo Lee & Eujin-Julia Kim & Sang-Woo Lee, 2017. "Examining Spatial Variation in the Effects of Japanese Red Pine ( Pinus densiflora ) on Burn Severity Using Geographically Weighted Regression," Sustainability, MDPI, vol. 9(5), pages 1-18, May.
    2. George Chalamandaris & Nikos E. Vlachogiannakis, 2018. "Are financial ratios relevant for trading credit risk? Evidence from the CDS market," Annals of Operations Research, Springer, vol. 266(1), pages 395-440, July.
    3. Tuo Sun & Shihao Zhu & Ruochen Hao & Bo Sun & Jiemin Xie, 2022. "Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms," Mathematics, MDPI, vol. 10(14), pages 1-22, July.
    4. Ma, Tao & Zhou, Zhou & Abdulhai, Baher, 2015. "Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 27-47.

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