A Decision Making Framework for Recommended Maintenance of Road Segments

TL;DR

This paper introduces a comprehensive decision-making framework for road maintenance that leverages predictive modeling, multi-factor prioritization, and deep reinforcement learning to optimize maintenance planning under budget constraints.

Contribution

It presents a novel framework integrating performance prediction, multi-criteria prioritization, and deep reinforcement learning for more scientific road maintenance decisions.

Findings

Improved prioritization of maintenance routes and sections.

Enhanced decision accuracy with deep reinforcement learning.

Framework adapts to limited budgets and historical data.

Abstract

Due to limited budgets allocated for road maintenance projects in various countries, road management departments face difficulties in making scientific maintenance decisions. This paper aims to provide road management departments with more scientific decision tools and evidence. The framework proposed in this paper mainly has the following four innovative points: 1) Predicting pavement performance deterioration levels of road sections as decision basis rather than accurately predicting specific indicator values; 2) Determining maintenance route priorities based on multiple factors; 3) Making maintenance plan decisions by establishing deep reinforcement learning models to formulate predictive strategies based on past maintenance performance evaluations, while considering both technical and management indicators; 4) Determining repair section priorities according to actual and suggested repair effects. By resolving these four issues, the framework can make intelligent decisions regarding optimal maintenance plans and sections, taking into account limited funds and historical maintenance management experiences.