Optimal Scheduling of Road Maintenance Jobs Considering Impact on Traffic Flows

Planning road maintenance at scale requires repeatedly simulating how traffic redistributes when capacity is reduced --- a process that's normally too computationally expensive to embed directly into scheduling optimization. This paper builds data-driven surrogate models that predict equilibrium traffic flows directly from origin-destination demand, bypassing expensive equilibrium solvers, and validates the approach using real traffic data from Newark, NJ. This offers city planners and transportation departments a scalable tool for smarter maintenance scheduling that minimizes disruption, with potential extension to broader infrastructure and network planning problems. Authors: Charitha Nandepu, Lohitha Kalepu, Gabriele Ciavarella, SangWoo Park Paper: https://arxiv.org/abs/2608.14491v1