Optimal Scheduling of Road Maintenance Jobs Considering Impact on Traffic Flows

Charitha Nandepu; Lohitha Kalepu; Gabriele Ciavarella; SangWoo Park

eess.SYAug 14, 2026

Optimal Scheduling of Road Maintenance Jobs Considering Impact on Traffic Flows

Authors: Charitha Nandepu, Lohitha Kalepu, Gabriele Ciavarella, SangWoo Park

Abstract

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.

Figures & tables

Figure 1: Equilibrium OD flow visualization for Day 1, Hour 8. Edge width reflects flow magnitude, while color encodes log-scaled flow intensity, with brighter colors indicating higher equilibrium flows.

Model	RMSE	$R^{2}$	High-flow RMSE
	(veh/hour)		( $>200$ veh/hour)
MLP	84.32	0.9088	605.93
CNN	133.84	0.7703	983.82
GNN	58.48	0.9561	167.20
Attention-based NN	35.87	0.9835	236.22

Table 1: Performance comparison of models for equilibrium flow prediction

Surrogate Modeling

Surrogate modeling builds faster or more interpretable approximations of expensive simulations and black-box models.

See all 227 papers

Simulation-Free Estimation of Traffic Flows from Sparse Count Data
Jun 22, 2026Davide Guastella, Gianluca BontempiIntelligent Transportation Systems Traffic Flow Estimation
Aix Marseille University, CNRS, LIS, Marseille, France · Université Libre de Bruxelles, Machine Learning Group, Brussels, Belgium · WEL Research Institute, Wavre, Belgium
Metalearning traffic assignment for network disruptions with graph convolutional neural networks
Mar 6, 2026Serio Agriesti, Guido Cantelmo, Francisco Camara PereiraTraffic Flow Estimation Intelligent Transportation Systems
Department of Technology, Management and Economics, Technical University of Denmark, Lyngby, Denmark
Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction
Feb 25, 2026Xiannan Huang, Quan Yuan, Chao YangTraffic Forecasting Time Series Forecasting
Key Laboratory of Road and Traffic Engineering, Ministry of Education at Tongji University, 4800 Cao’an Road, Shanghai, 201804, Shanghai, China · Urban Mobility Institute, Tongji University, 1239 Siping Road, Shanghai, 200092, Shanghai, China

Jun 22, 2026J/K move · Enter open · S save

Simulation-Free Estimation of Traffic Flows from Sparse Count Data

Davide Guastella, Gianluca Bontempi

Aix Marseille University, CNRS, LIS, Marseille, France · Université Libre de Bruxelles, Machine Learning Group, Brussels, Belgium · WEL Research Institute, Wavre, Belgium

Intelligent Transportation Systems Traffic Flow Estimation

Open paper AskPDF arXiv

We propose a method for estimating time-varying traffic flow patterns from sparse aggregated vehicle counts. The method partitions the study area into spatial regions, constructs a set of feasible region-to-region routes, and solves a weighted least-squares optimization problem to determine the number of vehicles to allocate on each route. A weighted contribution matrix encodes sensor coverage, steering the optimizer toward flow configurations that are directly observable by sensors. Edge-level trajectories are then derived by scoring candidate routes against the temporal and volumetric profiles of aggregated regional sensor counts. The method is evaluated on the Brussels road network using real and synthetic traffic data. Results show that the proposed approach reproduces the daily traffic profile in the input data and outperforms the baseline methods at a fraction of the computational cost.

Abstract

Surrogate Modeling

Explore similar work

Simulation-Free Estimation of Traffic Flows from Sparse Count Data

Metalearning traffic assignment for network disruptions with graph convolutional neural networks

Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction