Generating Near-Optimal Road Condition–Capacity Improvement Decisions Using Monte Carlo Simulations
Publication: Journal of Infrastructure Systems
Volume 30, Issue 4
Abstract
Road travel cost can be defined as a function of condition and volume-capacity factors. Asset managers intervene on heavily trafficked and poor condition roads based on criteria to optimize network travel and intervention (social) costs. These criteria may involve a trade-off between improving the road condition or capacity. Road performance is known through periodic inspection and stochastic modeling to estimate a deteriorated future condition. The predicted future condition and traffic growth rates change pavement section intervention (capacity or condition improvement) priority over time. The optimal road intervention choice can be determined using algorithms, including the greedy algorithm and Monte Carlo simulations. Greedy algorithms search through the entire sample space locally and stepwise to approximate global optima, whereas Monte Carlo simulations randomly sample candidate sections to generate more globally optimum interventions. This study proposes a road asset management model using Monte Carlo methods to optimally choose road network interventions considering condition and traffic changes over a planning horizon. The study includes an empirical application using real world data and compares the proposed Monte Carlo simulations approach to the greedy algorithm.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments. The restricted data include Ugandan pavement condition, travel time, traffic volume, and inventory data.
Acknowledgments
The Uganda National Roads Authority (UNRA) kindly provided Ugandan national road data including condition, travel time, traffic volume, and pavement inventory data for this study.
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Published In
Journal of Infrastructure Systems
Volume 30 • Issue 4 • December 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Sep 11, 2023
Accepted: Jul 24, 2024
Published online: Sep 26, 2024
Published in print: Dec 1, 2024
Discussion open until: Feb 26, 2025
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