Decision Methodology for Allocating Funds across Transportation Infrastructure Assets
by Nasir G. Gharaibeh, P.E., M.ASCE, (Asst. Prof., Civ. Engrg. Dept., Univ. of Texas at El Paso, E201D 500 W. University Ave., El Paso, TX 79968. E-mail: ngharaibeh@utep.edu), Yi-Chang Chiu, M.ASCE, (Asst. Prof., Civ. Engrg. Dept., Univ. of Texas at El Paso, E201P 500 W. University Ave., El Paso, TX 79968. E-mail: chiu@utep.edu), and Patrick L. Gurian, (Asst. Prof., Civ., Arch., and Envir. Engrg. Dept., Drexel Univ., 3141 Chestnut St., Philadelphia, PA 19104. E-mail: pgurian@drexel.edu)
Journal of Infrastructure Systems, Vol. 12, No. 1, March 2006, pp. 1-9, (doi 10.1061/(ASCE)1076-0342(2006)12:1(1))
Download fulltext
Purchase Subscription
Permissions for Reuse 
View Issue Table of Contents
| Document type: |
Journal Paper |
| Abstract: |
One of the main activities in transportation infrastructure asset management is the allocation of available funds across infrastructure classes (e.g., pavements, bridges, signs) or programs (e.g., maintenance, construction). A methodology for allocating funds across transportation asset classes using multiattribute utility theory has been developed and is provided in this paper. This methodology can be used for performing trade-off analysis among asset classes where it is practical to consider potential shifts in funding from one class to another. The methodology was applied to a sample state highway network in Champaign County, Illinois. The sample highway network consists of pavements, bridges, culverts, signs, and intersections. Four funding allocation alternatives were evaluated using the developed methodology. The case study identified the funding allocation alternative that results in the lowest risk of infrastructure failure or poor performance (i.e., highest utility) from an experienced engineer standpoint. The utility analysis revealed that the decision maker in the case study is risk averse when managing infrastructure classes with the most potential to affect traffic safety and to be noticed by the public, such as bridges and intersections. The case study also revealed that the level of available funding and the level of infrastructure performance affect how the total funding is allocated among asset classes. |
|
