Safety-oriented pavement performance thresholds: Accounting for unobserved heterogeneity in a multi-objective optimization and goal programming approach

Panagiotis Ch Anastasopoulos, Md Tawfiq Sarwar, Venky N. Shankar

Research output: Contribution to journalArticle

24 Scopus citations

Abstract

The cornerstone of transportation infrastructure asset management is managing the physical infrastructure, with pavement preservation being one of the most critical and costly assets. Preserving pavements in an appropriate manner extends their service life, and most importantly improves motorists’ safety and satisfaction while saving public tax dollars. To that end, this paper presents a methodology to estimate pavement performance thresholds that are cost-effective and safe for users. Using data from Indiana, the relationships of the three criteria, i.e., safety (accident rates), normalized treatment cost and pavement service life, with the pavement performance (roughness, rutting, overall rating, and surface deflection), road geometry, traffic characteristics and climate - are investigated and estimated. These relationships are utilized in a multi-objective optimization and goal-programming scheme to identify performance threshold values that trigger preservation treatments. These analytically determined threshold values are found to be comparable to historical thresholds and thresholds derived from experts’ and users’ opinions.

Original languageEnglish
Pages (from-to)35-47
Number of pages13
JournalAnalytic Methods in Accident Research
Volume12
DOIs
StatePublished - Dec 1 2016

Keywords

  • Goal programming
  • Hazard-based duration modeling
  • International roughness index
  • Multi-objective optimization
  • Pavement performance thresholds
  • Pavement preservation
  • Pavement service life
  • Random parameters
  • Rutting
  • Surface deflection
  • Tobit regression
  • Unobserved heterogeneity

Fingerprint Dive into the research topics of 'Safety-oriented pavement performance thresholds: Accounting for unobserved heterogeneity in a multi-objective optimization and goal programming approach'. Together they form a unique fingerprint.

  • Cite this