## 4 Conclusions  
This study is based on the analysis of the current research status of domestic network-level road maintenance decision making and establishes an optimization model for road maintenance decision making that comprehensively considers maintenance funds, road performance, user costs, and environmental costs. Based on case analysis, the following conclusions are drawn:  
1. Based on existing research, the user costs that affect the effectiveness of road maintenance were determined as the cost of vehicle travel time and the cost of vehicle fuel consumption, and quantitative models were established for each.
2. Based on a user cost quantification model that affects the effectiveness of pavement maintenance, and applying the AHP model, an evaluation system for the effectiveness of highway asphalt pavement maintenance was established. A network-level pavement maintenance decision-making optimization model was established with the objective function of maximizing comprehensive maintenance benefits.
3. Taking a certain road network in a certain province as an example for network-level maintenance decision making, the final maintenance plan was obtained using Lingo software. The case results validate the effectiveness of the proposed optimization model, indicating that the research findings of this article can assist decision makers in making road maintenance decisions more reasonably by considering the diversity of practical problems from multiple perspectives.  
In addition, this study also has the following shortcomings: Firstly, when analyzing the quantification of vehicle operating costs, this article only considers one type of passenger car. In order to construct a more comprehensive user cost quantification model from a more comprehensive perspective, future research can increase the consideration of other vehicle models. Secondly, when building the network-level pavement maintenance decision model, this paper does not consider the user costs caused by the closure or semi-closure of road sections due to construction barriers. Finally, for future research, more components of user costs (e.g., vehicle safety costs and vehicle tire wearing costs) and environmental costs (traffic noise pollution costs) can be incorporated into the optimization model.  
Conceptualization, X.G.; Methodology, X.G. and H.Z.; Software, X.Z.; Validation, X.Z.; Formal analysis, X.D.; Resources, X.D. and Y.B.; Data curation, M.S.; Writing--original draft, X.G.; Writing--review & editing, M.S. and Y.B.; Supervision, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Combination**} & \multicolumn{8}{c}{**Road Number**} \\ \cline{2-10}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline
1 & & & II & & II & & II & & II \\
2 & II & II & & & II & II & II & II \\
3 & & & II & & II & V & V & V & V \\
4 & II & II & & & II & & V & \\
5 & II & & & & & II & & V & \\
6 & & V & & & & & & & \\
7 & & & & & & & & & \\
8 & & V & & & & & & & \\
9 & & & & & & & & & \\
10 & III & III & III & III & III & III & III & III & III \\
11 & V & V & V & V & V & V & V & V & V \\ \hline \hline \end{tabular}
\end{table}
Table 16: Optimal maintenance methods on the different road sections under different combinations.