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Statistical Analyses of Freeway Traffic Flows

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  • Tebaldi, Claudia
  • West, Mike
  • Karr, Alan F

Abstract

This paper concerns the exploration of statistical models for the analysis of observational freeway flow data, and the development of empirical models to capture and predict short-term changes in traffic flow characteristics on sequences of links in a partially detectorized freeway network. A first set of analyses explores regression models for minute-by-minute traffic flows, taking into account time of day, day of the week, and recent upstream detector-based flows. Day- and link-specific random effects are used in a hierarchical statistical modelling framework. A second set of analyses captures day-specific idiosyncrasies in traffic patterns by including parameters that may vary throughout the day. Model fit and short-term predictions of flows are thus improved significantly. A third set of analyses includes recent downstream flows as additional predictors. These further improvements, though marginal in most cases, can be quite radically useful in cases of very marked breakdown of freeway flows on some links. These three modelling stages are described and developed in analyses of observational flow data from a set of links on Interstate Highway 5 (I-5) near Seattle. Copyright © 2002 by John Wiley & Sons, Ltd.

Suggested Citation

  • Tebaldi, Claudia & West, Mike & Karr, Alan F, 2002. "Statistical Analyses of Freeway Traffic Flows," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(1), pages 39-68, January.
    • Handle: RePEc:jof:jforec:v:21:y:2002:i:1:p:39-68
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    Cited by:

    1. Y. F. Tang & William H. K. Lam & Mei-Lam Tam, 2006. "Comparison of two non-parametric models for daily traffic forecasting in Hong Kong," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(3), pages 173-192.
    2. Mike West, 2020. "Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 1-31, February.
    3. Catriona M. Queen & Ben J. Wright & Casper J. Albers, 2008. "Forecast covariances in the linear multiregression dynamic model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 175-191.
    4. Chen, Yi-Ting & Sun, Edward W. & Chang, Ming-Feng & Lin, Yi-Bing, 2021. "Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0," International Journal of Production Economics, Elsevier, vol. 238(C).

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