###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research** & **Main Research** & **Objective** & **Limitations** \\ \hline \multirow{8}{*}{Preventive maintenance} & [PERSON] et al. [10] & Established a multi-state system model based on Markov stochastic processes & Relied on Markov process assumptions, which may not accurately describe the degradation behaviors of all equipment \\ \cline{2-3}  & [PERSON] et al. [11] & Modeled the reliability of key components using the Weibull distribution & Only modeled key components; did not consider global optimization \\ \cline{2-3}  & [PERSON] et al. [12] & Emphasized the wide application of Weibull distribution in mechanical and electronic component failure analysis & Required accurate failure data, and Weibull distribution may not be applicable to all equipment types \\ \hline \multirow{8}{*}{[PERSON] et al. [13]} & Used a fast genetic algorithm to optimize driver scheduling & Only focused on driver scheduling; did not integrate with maintenance optimization \\ \cline{2-3}  & [PERSON] et al. [14] & Introduced spatiotemporal network theory to optimize vehicle flow allocation & Primarily focused on vehicle flow; did not consider the impact of maintenance on scheduling \\ \hline \multirow{8}{*}{[PERSON] et al. [15]} & Optimized train turnaround through multi-train coordinated scheduling & Did not consider the impact of maintenance plans on train availability \\ \cline{2-3}  & [PERSON] et al. [16] & Introduced a multi-train allocation optimization model considering operational costs, service quality, and equipment reliability & Focused on economic aspects; did not detail maintenance impact \\ \cline{2-3} Train scheduling & [PERSON] et al. [17] & Optimized train scheduling with the objective of minimizing empty train mileage & Ignored the constraints imposed by maintenance plans on train scheduling \\ \cline{2-3}  & [PERSON] et al. [18] & Used an improved branch-price algorithm to optimize train operations & High computational complexity; difficult to scale to large networks \\ \hline \multirow{8}{*}{[PERSON] et al. [19]} & Built a comprehensive train and crew scheduling model using spatiotemporal networks for resource allocation & Converted subproblems into solving path-constrained problems; high computational complexity \\ \cline{2-3}  & [PERSON] et al. [20] & Constructed a minimum-cost maximum-flow optimization model with spatiotemporal networks to improve computational efficiency & Primarily optimized train operations; did not consider maintenance needs in detail \\ \cline{2-3}  & & Constructed a minimum-cost maximum-flow optimization model with spatiotemporal networks to improve computational efficiency & Primarily optimized train operations; did not consider maintenance needs in detail \\ \cline{2-3}  & & Constructed a minimum-cost maximum-flow optimization model with spatiotemporal networks to improve computational efficiency & Primarily optimized train maintenance needs in detail \\ \cline{2-3}  & & Constructed a minimum-cost maximum-flow optimization model with spatiotemporal networks to improve computational efficiency & Primarily optimized train operations; did not consider maintenance needs in detail \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The remainder of this paper is structured as follows. Section 2 describes the construction of the spatiotemporal network for subway vehicle maintenance and scheduling. Section 3 details the joint optimization model based on spatiotemporal network graphs. Section 4 presents the improved genetic algorithm for model solution derivation. Section 5 conducts case studies, and Section 6 concludes the paper.