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

Lu Sun ()
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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

Transportation Science, 2016, vol. 50, issue 3, 998-1015

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.

Keywords: long-term traffic forecast; headway; piecewise stationary renewal process; stochastic projection and factoring; structural time series analysis (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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