# Optimal Scheduling of Road Maintenance Jobs Considering Impact on Traffic Flows

arXiv: https://arxiv.org/abs/2608.14491 · HTML: https://mlsanity.com/paper/2608.14491/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 14 Aug 2026.

## Builds on (most cited first, 2 of 2)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [User equilibrium traffic assignment: k paths subtracting-adding algorithm](https://mlsanity.com/paper/2107.04018/) (2021)

## Abstract (arXiv)

Network-level maintenance planning requires repeated evaluations of equilibrium traffic flows under road capacity reductions. While equilibrium traffic assignment models are well established, their repeated solution quickly becomes computationally prohibitive and challenging to embed within maintenance scheduling problems. This paper investigates data-driven surrogate models that approximate equilibrium arc flows directly from origin-destination demand, using optimization-based equilibrium solutions as ground truth. A real-world case study based on traffic data from the Newark, New Jersey area demonstrates the effectiveness of the proposed approach as a scalable building block for future maintenance scheduling frameworks.