Maintaining rural highways is crucial in ensuring the reliability and efficiency of transportation infrastructure in modern rural areas. Rural highways often suffer heavy traffic from logistics and regular transportation users. The efficient management of these roads is essential to avoid issues like traffic bottlenecks, fuel consumption, and environmental problems. Traditional maintenance approaches focus on cost reduction, which can lead to adverse effects such as network congestion and environmental damage. To address these challenges, this study proposes a bi-level mathematical programming model aiming at optimizing rural highway maintenance. This model balances maintenance costs, network congestion, system fuel consumption, and environmental impacts. By transforming the bi-level model into a single-level mixed-integer linear programming model, the study enhances the computational feasibility, enabling practical implementation using commercial solvers. The model’s effectiveness is validated through numerical examples, providing insights for the development of optimal maintenance schedules that minimize externality costs while adhering to financial constraints and operational guidelines, providing a valuable addition to the road engineer’s toolbox.

A mature theoretical model is an integral component of pavement management systems. Typically, such models encompass pavement performance prediction, cost–benefit analysis of maintenance measures, and decision-making frameworks for maintenance strategies. This paper primarily investigates the economic analysis and decision planning aspects of pavement maintenance measures. Initially, considering greenhouse gas emissions, a comprehensive cost evaluation is conducted for three typical maintenance measures (microsurfacing, slurry sealing, and milling with overlay), encompassing carbon emission costs, delay costs, and construction costs, to establish comprehensive cost indicators for subsequent decision optimization models. Subsequently, an optimization algorithm with Clip pruning is introduced for near-end strategy optimization, along with the development of suitable evaluation functions for maintenance measure effectiveness, thereby establishing a reinforcement learning-based optimization model for pavement maintenance decisions. Regarding the model results, the costs of microsurfacing, 4 cm AC slurry seal, 4 cm SMA slurry seal, and 4 cm milling with overlay are 10 ¥(m2), 50 ¥(m2), 66 ¥(m2), and 60 ¥(m2), respectively. Furthermore, by comparing the reinforcement learning decision model developed in this study with the Q-learning algorithm, the model demonstrates a 60% improvement in convergence speed and a 20% increase in average reward.