A Model to Forecast Peak Spreading

About the report:

As traffic congestion increases, the K-factor (the proportion of the 24-hour traffic volume that occurs during the peak hour), may decrease.  This behavioral response is known as peak spreading: as congestion grows during the peak travel times, motorists may shift their departure time to a non-peak hour.  Knowing whether K-factors will remain constant will affect the estimation of travel demand, and the resultant transportation performance, since the traffic volume during a given hour may affect travel speed and vehicle emissions.

The purpose of this study was to develop a model for forecasting peak spreading whereby peak spreading is measured as change in the K-factor.  Data were collected from 32 continuous count stations in the six Northern Virginia counties of Arlington, Fairfax, Fauquier, Loudoun, Prince William, and Stafford for the period 1997-2010.  Because some stations gave two-directional counts and some gave only one-directional counts, there were 52 station-direction combinations, or sites, for analysis purposes.  The data collected showed that the average annual K-factor adjusted for months for which data were not available decreased by an average of 0.006 (p < 0.01), from 0.103 to 0.097, during the period.  The 24-hour volume-to-capacity ratio, which is a surrogate for travel congestion, increased by an average of 0.7 (p < 0.01), from 7.3 to 8.0.  Both changes were statistically significant. 

Two models to forecast K-factors were developed in this study.  Model 1, for use with an established roadway with an existing K-factor, explained 88% of the variation in K-factors and is based on the previous K-factor, the percentage increase in the jurisdiction’s employment, and the roadway functional class.  Model 2, for use with a new roadway without an existing K-factor, explained 66% of the variation in K-factors and is based on the percentage change in the jurisdiction’s employment; whether the route is radial or circumferential; and for freeways, the 24-hour volume-to-capacity ratio.  Use of these variables is advantageous as they are typically available when a 10-year forecast is made.  The two models have three implications for forecasting peak spreading.  First, site characteristics (e.g., 24-hour volume-to-capacity ratio) and regional socioeconomic characteristics affect the K-factor.  Second, the 24-hour volume-to-capacity ratio affects the forecasts, even though the effect is evident only after controlling for other variables.  Third, the K-factor varies more across sites with the time period held constant than across time periods with the site held constant.  The study recommends that VDOT consider the use of the two models when more detailed data are not available; their use would provide an empirically based alternative to assuming the K-factor will remain constant.

A potential study limitation is that congestion during the “before” period in Northern Virginia was already so great that any congestion-based effects on peak spreading had already occurred.  However, as the large variability in K-factors across sites dampened the overall effect of congestion, it may be the case repeating this study in other locations would yield similar results.

Authors