Title

New Capital Cost Table for Highway Investment Economic Analysis

Document Type

Journal Article

Publication Date

2005

Subject Area

economics - capital costs

Keywords

Tables (Data), Investments, Investment requirements, Improvements, Highways, Highway Economic Requirements System, Economic analysis, Cost data, Cost benefit analysis, Construction costs, Capital costs, Benefit cost analysis

Abstract

The Federal Highway Administration (FHWA) maintains the Highway Economic Requirements System (HERS) model to enable the estimation of highway investment requirements for the biennial "Status of the Nation’s Highways, Bridges, and Transit: Conditions and Performance" report to Congress. The HERS logic relies on the application of benefit–cost analysis to evaluate and select the best set of roadway improvements for systemwide implementation. Benefits of roadway improvements are calculated from user and agency cost savings (including externalities), and cost is considered simply the capital cost of the improvement. The current research seeks to improve the knowledge of the unit capital costs of improvements modeled in HERS with the use of as-built road construction cost data previously developed for FHWA and bid–schedule data published by various highway agencies. The construction cost dataset used contained detailed data on more than 2,500 construction projects in six states; however, it lacked enough samples to apply either econometrics or generalized statistical analysis to make reliable estimates for all roadway improvement and cost categories in HERS. Both deterministic and probabilistic methods were applied to engineering specifications along with bid–schedule data on various roadway improvement projects to generate capital cost data for each of the classification variables. The final cost table data developed resulted in national highway improvement capital cost data that most realistically corresponded with a typical cost variation across land terrain and urban population density for a given improvement type. For some classification variables the new cost data are 60% to 150% higher than the previous HERS cost estimates.