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Evaluating Alternative Operations Strategies to Improve Travel Time ...

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SHRP 2 L11: Final Appendices<br />

• The Gumbel distribution is unbounded and can have its mass either in the lower<br />

or upper portion of the distribution.<br />

• The Fréchet distribution, in contrast, has most of its mass at low values, has a<br />

lower limit, and has an unlimited upper tail.<br />

• The Weibull distribution has most of its mass in the upper tail of the distribution<br />

and takes on a maximum value.<br />

The Generalized EV probability density function formulation is presented in Equation 1.<br />

It has three parameters: the location (µ), scale (σ) and shape (ξ) parameters. These<br />

parameters can be used <strong>to</strong> distinguish the three distribution types. Similar <strong>to</strong> the mean and<br />

standard deviation of a normal distribution, the location parameter µdetermines the<br />

"location" of the distribution, shifting the distribution <strong>to</strong> the right or left, without<br />

changing the shape of the distribution. The scale parameterσdetermines the spread of the<br />

distribution. The shape parameter ξ controls the shape of the distribution—in particular<br />

the tail behavior of the distribution.<br />

The Figure C.1 below illustrates the standard shapes of the respective distributions for<br />

µ=0, σ=1 and ξ=-0.5 (Weibull), ξ= 0.5 (Fréchet) and ξ→0 (Gumbel). Since these are the<br />

standard distributions, the distributions are located over zero and have a standard scale<br />

parameter of one, with the lower bound for the Fréchet distribution at -2 and the upper<br />

bound for the Weibull distribution at +2. For rare events, the Fréchet or Gumbel<br />

distributions have the most appropriate shapes since most of the mass will occur at the<br />

lower tail of the distribution and the extreme observations will occur in the upper tail. In<br />

practice, the Gumbel distribution is the distribution that is most widely used <strong>to</strong><br />

characterize rare events because it can be estimated using a two parameter specification<br />

(location and scale). The Gumbel probability density function is presented in Equation 2.<br />

VALUATION OF TRAVEL-TIME RELIABILITY FOR RARE EVENTS Page C-3

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