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A Policy On Geometric Design Of Highways And Streets 2011 !FREE!

Operating speed models have often been used to assess geometric design consistency, most notably on two-lane rural highways. Many studies have estimated statistical models to predict vehicle operating speeds that may be used to evaluate highway design consistency. In many of the models, variables such as roadway geometric features, posted speed limit, and annual average daily traffic (AADT) can be input into the models to determine the vehicle operating speed under free-flow conditions (e.g., vehicle headways of 4 or more sec). While the most common speed output from these models is the 85th-percentile speed, statistical models of mean speed and the standard deviation of speed exist. Applying operating speed models may confirm that designated design speeds, posted speed limits, and driver expectations will all be more consistent when the roadway geometry is designed to manage speeds. (TRB 1998)

a policy on geometric design of highways and streets 2011

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The design speed concept does not necessarily guarantee design consistency. The Green Book recommends minimum or limiting values for many speed-based geometric-design elements. When the geometric design values are larger than minimum values, the result is a higher inferred speed, which may be associated with higher operating speeds. This may produce instances where operating speeds on adjacent roadway segments are large or instances when the operating speed differs significantly from the designated design speed used to establish the geometric design features of the roadway. A more detailed explanation of design consistency can be found in later sections of this report.

This chapter examines the relationship between speed and geometric design. The different elements of geometric design, such as horizontal alignment, vertical alignment, and cross-section design elements, are related to speed. The chapter outlines how the designated design speed is related to horizontal- and vertical-curve design criteria, the criteria for selecting cross-section elements, such as lane width, and the relationship between the designated design speed and inferred design speed. The geometric design features of a roadway subsequently influence operating speeds. In addition to discussing how geometric elements are associated with the designated design speed, this chapter describes various operating speed models that have been reported in the literature; this includes mean speed, speed dispersion, and 85th-percentile operating speed. There are examples of how to use operating speed prediction models to evaluate how geometric elements and other roadway characteristics affect driver speed choice. In addition to the illustrative examples shown in this chapter, other speed prediction models are shown in appendix A.

Horizontal curve design is governed by the point-mass model, which prescribes a minimum radius of curvature as a function of the designated design speed, maximum superelevation of the roadway, and maximum side friction factor. (AASHTO 2011) The friction factor used in the geometric design of highways and streets is a demand value that is based on driver comfort thresholds rather than the side friction supply at the tire-pavement interface. The Green Book recommends limiting values for superelevation and side friction factor for horizontal-curve design based on the designated design speed. The radius of curvature equation found in the Green Book is shown in figure 5.

Another fundamental geometric design criterion is the SSD, which is the distance needed for a driver to see an object on the roadway in front of the vehicle, react to it, and brake to a complete stop. The SSD is composed of two measures: (1) the distance traveled during perception-reaction time, and (2) the distance traveled during braking. Minimum SSD criteria are based on the assumptions that drivers travel at a speed equal to or below the designated design speed.

Objects located along the inside of horizontal curves may pose a visual sight obstruction, which is also considered in horizontal-curve design. (AASHTO 2011) This is assessed using the HSO, which is determined as follows in figure 9.

While there are numerous statistical models that estimate or predict 85th-percentile operating speeds as a function of geometric design features, as shown in the next section, few models are available to predict mean operating speeds. The mean speed can be used to estimate the 85th-percentile speed, if speeds are normally distributed, by adding the standard deviation of speed to the mean speed. (Roess et al. 2011) This enables the opportunity to assess the association between speed dispersion and geometric design features in a statistical model. This section of the guidance report shows several examples of statistical models that include mean speed and speed dispersion metrics as a function of geometric design features. In each case, the speed metric (i.e., posted speed limit, mean speed, or standard deviation of speed) is the dependent variable, while roadway geometric features and other site-specific features are the independent variables in the model. All of the models are linear models, where the dimension of the independent variable is multiplied by a regression coefficient to determine how the roadway design features influences the expected speed metric. Several other statistical models of vehicle operating speeds are shown in appendix A.

Himes et al. (2011) used a system of linear equations to estimate models for the posted speed limit, mean speed, and standard deviation of speed. An interpretation of the models is provided in table 5. Data were collected on urban and rural two-lane undivided highways in Virginia and Pennsylvania. These data included roadway characteristics, vehicle operating speeds, and hourly traffic flow rates. An example of a typical linear model used by Himes et al. (2011) for their system of simultaneous equations is shown in figure 10. The linear model is used with the information provided in table 5.

Similarly, a study by Figueroa Medina and Tarko (2005) estimated statistical models that considered the combined effect of mean speed and speed deviation to predict percentile operating speeds. The free-flow speed models were developed for tangent segments and horizontal curves on two-lane rural highways. The data used to develop the ordinary least squares (OLS) regression model were collected in Indiana and included roadway geometric design features, free-flow speeds, and sight distances. Statistical models were estimated for operating speeds on tangent sections and operating speeds on horizontal curves. The equation shown in figure 11 was developed to predict operating speeds on two-lane rural highway tangent sections.

Drivers select operating speeds based on multiple factors, several of which include the roadway design features. The parameters shown in table 6 generally show that more restrictive geometrics and roadways that have built-up adjacent land use (such as residential and commercial developments) tend to be associated with lower operating speeds.

An example using the models provided by Figueroa Medina and Tarko (2005) and Himes et al. (2011) that estimate mean speed and speed dispersion/deviation is shown in table 7 through table 12. Figueroa Medina and Tarko (2005) determined two distinct models for mean speed and speed dispersion: one for tangent segments, one for horizontal curves. Both are shown in the tables. These examples illustrate how to apply operating speed models to predict driver speed choice on two-lane rural highways. Operating speed prediction models may be used in methods 1 through 4 of the self-enforcing roadway design concepts presented in chapter 5. In table 7 through table 12, the coefficient is multiplied by the dimension to produce a mean speed estimate associated with the dimensions. All these associations are added to produce the predicted mean speed on tangent- or horizontal-curve segments.

The 85th-percentile speed represents the speed at which 85 percent of vehicles are traveling at or below under free-flow conditions. This value can be used to establish posted speed limits, as recommended by the Manual on Uniform Traffic Control Devices for Streets and Highways (MUTCD) or to evaluate the design consistency of a roadway. (FHWA 2009) Numerous studies have estimated linear regression models to predict 85th-percentile speeds on horizontal curves and tangents. Several geometric design features as well as the posted speed limit have been included in the speed-prediction models. A summary of these models for two-lane rural highways is provided below. Like the mean and speed dispersion models shown in the previous section of this guidance report, 85th-percentile operating-speed-prediction models can be used to estimate driver speed choice in methods 1 through 4 of the self-enforcing roadway design concepts presented in chapter 5.

Krammes et al. (1995) collected speed and geometric design data along horizontal curves and approach tangents in five States. The data were used to develop a model to predict operating speeds on both curves and approach tangents, and these models were then used to evaluate design consistency between successive geometric features. The geometric features included in the regression models of 85th-percentile operating speed were the degree of curvature, length of curvature, deflection angle, and in some cases, the 85th-percentile speed on approach tangents. The study determined that an increase in the degree of curvature and deflection angle results in a decrease in 85th-percentile speeds on the curve. For curves less than or equal to 4 degrees, as the length of the curve increases, the 85th-percentile speeds on the curve increase, while for curves greater than 4 degrees, as the length of the curve increases, the 85th-percentile operating speeds on the curve decrease. Additionally, as the 85th-percentile speed on the approach tangent increases, the 85th-percentile operating speeds on the curve increase. The equations developed from this study are shown in appendix A.


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