The nation’s low-volume road network can be compared to the Energizer Bunny . . . it just goes on and on, providing a valuable link between rural communities and markets, schools, doctors, recreational areas and other important facilities. For the most part, low-volume roads are managed by cities, counties and townships, although some state highway agencies also have responsibility for the management of this extensive road network. A good measure of the significance of low-volume roads is that of the U.S.’s estimated 3.95 million miles of road, 1.45 million are unpaved and over 3 million are considered rural rather than urban mileage.
Because of their lower traffic volumes and generally more rural nature, these roads typically receive less attention. When it comes to maintenance, rehabilitation and reconstruction of these facilities, the needs far outweigh the available funding, resulting in a growing number of deferred needs each year. Therefore, road agencies responsible for the management of low-volume roads are constantly searching for design and maintenance strategies that provide cost-effective solutions for maintaining their low-volume road network.
One strategy for controlling maintenance costs is to ensure that the most cost-effective road surface is selected for the facility. In some cases, this might result in upgrading an unsurfaced road to a paved road, but in other cases it might suggest that a better strategy is to downgrade the road.
Dakota decoding
One of the commonly encountered low-volume road challenges is the decision regarding the appropriate surface type. While hot-mix asphalt (HMA) roads may be preferred, gravel roads or surface-treated roads (called blotter roads by some) may be more cost-effective in the long run, especially for very low traffic volumes. However, relatively few agencies have the data available to decide when each surface type is appropriate for a given set of conditions.
To help local agencies within the state of South Dakota make the decision regarding the most cost-effective road surface, the South Dakota Department of Transportation (SDDOT) initiated a study to develop an easy-to-use methodology to compare the costs associated with three different road surfaces: HMA, blotter and gravel. Stabilized gravel roads also were considered in the analysis.
The resulting tools take into account both costs and other factors—such as politics, growth rates, housing densities, mail routes and industrial or truck traffic—that can be considered in selecting the appropriate road surface.
Two versions of the methodology are available for use by local agencies: a manual method and a computerized tool. Both approaches are based on a life-cycle cost analysis of construction, maintenance and user costs supplied by local South Dakota agencies that agreed to participate in the study. Average daily traffic and crash occurrence data also were provided to develop the cost models.
The manual version of the resulting tool, which is available in the form of a technical brief, is largely based on default treatment costs and frequencies obtained from the agencies in South Dakota, while the computerized version allows an agency to modify the costs and treatment frequencies to fit conditions anywhere in the country. The tools are being distributed by the South Dakota Local Transportation Assistance Program (SDLTAP) and the South Dakota Council of Governments (COG).
To determine the most cost-effective road surface, the user can analyze agency costs, user costs or the other considerations listed earlier (such as growth rates and housing densities). Based on the information provided by the agencies in South Dakota, the default cost and treatment frequency models indicate that the following surfaces are most cost effective at different average daily traffic (ADT) levels:
- Gravel roads are most cost effective at ADT levels of 0 to 150;
- Blotter roads are most cost effective at ADT levels between 150 and 660; and
- HMA roads are most cost effective at ADT levels greater than 660.
Based on these findings, local agencies in South Dakota may find it cost effective to upgrade their gravel roads at ADT levels greater than 150 when only agency costs are considered. A similar study conducted by the Minnesota Department of Transportation showed an ADT of 200 vehicles per day as the trigger level for considering the upgrade of a gravel road.
A more comprehensive evaluation of road-surfacing decisions recognizes that the costs to the users of the facility vary greatly depending on the road surface they are driving on. To account for this factor, an optional user-cost calculator is incorporated into the methodology. The user-cost tool compares the differences in vehicle operating costs and crash costs associated with driving on different roadway surfaces. As one might expect, vehicle operating expenses are lower on HMA-surfaced roadways and increase for blotter roads, stabilized gravel and gravel roads, respectively. The default vehicle operating cost models that are incorporated into the model are included as Fig. 1.
Crash costs are more difficult to consider in the model because of the number of factors that can cause crashes. Therefore, the model asks the user to define the potential for crashes on the roadway being considered. Depending on whether the crash potential is nonexistent, low, medium or high, crash costs are assigned on a per-mile basis using Fig. 2.
The total of the agency costs, vehicle operating costs and crash costs for each surface type represent the average cost of maintaining that surface type over a 20-year period. Based solely on costs, the most cost-effective surface type is that surface that provides the lowest total cost over the 20-year analysis period. Noneconomic factors also can be incorporated into the analysis using rating factors that might influence the final selection of a surface type. In the model, default rating factors include costs, political issues, growth rates, housing concentration/dust control, mail routes and industry/truck traffic. The importance of each rating factor is determined by the agency in terms of a percentage, so that the total percentage equals 100%. For example, the assignment of 0 to growth rates indicates that this rating factor is not important to the selection of road surface type in this example.
Once rating factors have been defined, the agency must assign scores for each factor based on each surface type being considered. Any scoring system can be used. If two surfaces are essentially equal in addressing a rating factor, the same score can be assigned to both surfaces. The product of the rating factor and the score is summed for each surface type, and the surface type with the highest score is recommended as the most appropriate road surface for the criteria incorporated into the analysis.
Computer talk
The computerized version of the model allows the user to analyze both economic and noneconomic factors that can be tailored to the types of maintenance treatments, treatment cycles and treatment costs that are most representative of actual conditions. For example, for each surface type being considered, the user has the ability to select the types of maintenance treatments that will be applied over the analysis period, the frequency with which the treatments will be applied and the cost of applying each treatment. The dialog box for entering agency costs associated with an HMA-surfaced road is shown in Fig. 3. Similar dialog boxes are available for entering agency costs for other surface types and user costs.
An example of the results of the analysis is presented in Fig. 4. Note that for each surface type, agency costs, vehicle operating costs and crash costs can be considered in determining the most economical road surface. Since some agencies do not have well-defined user costs (such as vehicle operating costs and crash costs), a user-cost weighting factor has been added to the analysis. This weighting factor is fully customizable to increase or decrease the effect of user costs in the cost calculations. In the example shown in Fig. 4, the blotter surface is the most economical road surface of the four surface types considered. If the user wants to consider noneconomic factors in addition to the cost factors, the user can select the “Additional Selection Criteria” button, accessing the same set of rating and scoring factors described earlier.
Some encouragement
Determining the most effective road surface on a low-volume road presents a challenge for many local road agencies. However, a project sponsored by the SDDOT resulted in the development of two tools that can be used to help agencies make this decision. Based primarily on default models that were customized to the treatment cycles and costs found in local agencies within the state of South Dakota, a manual method for analyzing both economic and noneconomic factors is available in a technical brief. A computerized version also is available. The computerized version provides additional benefits because the default models used in the analysis can be modified to fit conditions in virtually any location within the U.S. Both tools are available through the SDLTAP and the COG.
