Final Report (PDF)

Tech Brief (PDF)

Project Title	Strategic Prioritization and Planning for Multi-Asset Transportation Infrastructure Maintenance, Rehabilitation, and Improvements: Phase 2 – Data-driven Decisions from Continuous Monitoring
University	George Mason University University of Delaware Pennsylvania State University
Principal Investigator(s)	PI: Elise Miller-Hooks, George Mason University Co-PI: David Lattanzi, George Mason University Co-PI: Sue McNeil, University of Delaware Co-PI: Shelley Stoffels, Penn State Co-PI: Kostas Papakonstantinou, Penn State
Funding Source(s) and Amounts Provided (by each agency or organization)	George Mason University Federal Share: $60,000 George Mason University Match Share: $60,000 University of Delaware Federal Share: $30,000 University of Delaware Match Share: $30,000 Penn State Federal Share: $ 60,000 Penn State Match Share: $60,000
Total Project Cost	$300,000
Start and End Dates	05/21/2021 -03/31/2023
Brief Description of Research Project	This project will extend prior work by this team in phase one of this project, “Strategic Prioritization and Planning for Multi-Asset Transportation Infrastructure Maintenance, Rehabilitation, and Improvements: Phase 1 — Prioritization through Optimization.” Outcomes of the first phase will include: A deeper understanding of the nature of crowdsourced vehicle response data and its utility, specific to the perception of asset (roadway and bridge) condition, with its impact on free-flow speeds and capacities, and the ability to detect deteriorated conditions through latent space modelling of the data and developed machine learning algorithms. Development of probabilistic predictive models for multi-asset (pavement and bridge) roadway system serviceability levels, with and without maintenance or other improvements, while considering inspection accuracy needs, activity impacts and other associated costs. Conceptualization of the multi-asset, strategic planning of maintenance, repair and rehabilitation options (improvement actions) and their prioritization for implementation as a bi-level, stochastic mathematical program that accounts for: system-wide traffic impacts from reduced capacity from deterioration and construction work zones, and post-improvement increased capacity and speed (a user equilibrium is sought in a lower-level traffic assignment problem); and explicitly accounting for uncertainty in asset state over time due to stochastic evolution of deterioration processes (a Markov decision process problem formulation of the upper-level decision process involving probabilistic state transitions due to deterioration).