Multi-objective optimization of maintenance, repair, and rehabilitation schedule for pavements in urban networks considering greenhouse gas emissions

Abstract

A multi-objective optimization (MOO) problem of scheduling maintenance, repair, and rehabilitation (MRR) activities for pavements in an urban network while considering emissions is developed. The gaps that are addressed include accurately accounting for queue spillbacks that may arise due to MRR activities in urban areas to improve prediction of emissions. A bi-level optimization framework is utilized. At the lower level, the Link Transmission Model is used to analyticallly account for traffic flow dynamics and queue spillbacks due to MRR actions on links in an urban network. At the upper level, the population-based incremental learning algorithm is used. Various scenarios are simulated on an urban grid network of Philadelphia, PA. The results demonstrate that accounting for emissions in the MOO formulation leads to an MRR schedule with the lowest overall costs, despite slightly higher user costs. A Pareto frontier is developed to reveal the tradeoff between emissions and user-agency costs. Results suggest that emissions can be lowered by increasing user-agency costs until a certain point, after which only diminishing returns can obtained on emission reduction. The proposed methodology can provide valuable insights for agencies in improving overall network performance and, when considered together with broader social and economic impacts, enhancing sustainability.

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