A Markovian Optimization Model for Pavement Maintenance Using Policy Iteration Algorithm with Discounted Road-User and Agency Costs

Abstract

The widespread deterioration of roadways necessitates studying the adverse impact on energy consumption, vehicle maintenance, traffic flow, travel time, and the environment, and identifying effective maintenance strategies for localities. The rate at which pavement deteriorates is unpredictable, and modeling is both difficult and often inaccurate without applying an optimization technique such as the Markov decision model. Since pavement is an integral element of roadway infrastructure, more accurate prediction of deterioration rates is key for those involved and implementing an optimal maintenance strategy will help keep pavement in the best possible condition for as long as possible. Putting a good Pavement Management System (PMS) in place will maximize return on investment and/or minimize overall cost to society. Due to limited availability of funds for infrastructure development and maintenance, the issue of deciding how to approach road maintenance constantly arises. The objective of this study is to develop an optimum road maintenance policy by using the correlation between average vehicle speed and pavement roughness. The pavement condition data used was based on the International Roughness Index (IRI). The optimum road maintenance policy was based on the Markov Decision Model and then the policy iteration method was applied to address the questions of what maintenance actions will be required at a given state of the pavement. Developing an optimum road maintenance policy involved four major steps: (1) establishing a relationship between pavement roughness and vehicle speeds; (2) converting changes in speed into road user travel time; (3) determining increases in travel time with particular levels of pavement roughness and corresponding road user costs such as Vehicle Operating Cost (VOC) and lost wages; and (4) utilizing the aggregate road-user and agency cost to determine the optimum maintenance policy, using the District of Columbia as a case example. An opportunity cost analysis indicates that implementation of the recommended maintenance policy will result in a significant savings when compared with the existing District of Columbia’s road maintenance policy.