CIAM-UTC-REG38
Research Team
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 Sources
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
Agency ID or Contract Number
69A3551847103
Start and End Dates
05/21/2021 — 08/31/2022
Project Description
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).