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Stochastic Projection-Factoring Method Based on Piecewise Stationary Renewal Processes for Mid- and Long-Term Traffic Flow Modeling and Forecasting

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  • Lu Sun

    (Department of Civil Engineering, International Institute of Safe, Intelligent and Sustainable Transportation and Infrastructure, Catholic University of America, Washington, DC 20064; and Southeast University, Nanjing 210096, China)

Abstract

Forecasting traffic over a long period of time is of considerable interest and usefulness, but accurate forecasting is very difficult. Traditional projection and factoring methods for mid- and long-term cumulative traffic forecasting are deterministic and only provide a point prediction without specifying a statistical measure of prediction reliability. This paper constructs a stochastic projection and factoring method by casting long-term traffic volume counts into an integrated and rigorous framework of a more refined structural time series component model with piecewise stationary renewal processes capturing time-of-day, day-of-week, monthly, and yearly variations. By doing so, the new method roots itself in a solid theoretical foundation and generates two advantages. First, it results in a more accurate point prediction of cumulative traffic by taking into account the time-of-day traffic count variation in the modeling of unobservable future long-term traffic flow at temporary count stations or at a site under investigation as a mixture of piecewise stationary renewal processes with different means and variances. Second, it allows an interval prediction to be estimated by incorporating uncertainty into the modeling and forecasting process.

Suggested Citation

  • Lu Sun, 2016. "Stochastic Projection-Factoring Method Based on Piecewise Stationary Renewal Processes for Mid- and Long-Term Traffic Flow Modeling and Forecasting," Transportation Science, INFORMS, vol. 50(3), pages 998-1015, August.
  • Handle: RePEc:inm:ortrsc:v:50:y:2016:i:3:p:998-1015
    DOI: 10.1287/trsc.2015.0607
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    References listed on IDEAS

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    1. D. J. Buckley, 1968. "A Semi-Poisson Model of Traffic Flow," Transportation Science, INFORMS, vol. 2(2), pages 107-133, May.
    2. Andrew C. Harvey, 1990. "The Econometric Analysis of Time Series, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026208189x, December.
    3. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    4. Sun, Lu & Yang, Jun & Mahmassani, Hani, 2008. "Travel time estimation based on piecewise truncated quadratic speed trajectory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(1), pages 173-186, January.
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    Cited by:

    1. He, Yuxin & Zhao, Yang & Luo, Qin & Tsui, Kwok-Leung, 2022. "Forecasting nationwide passenger flows at city-level via a spatiotemporal deep learning approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).

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