## 1 Introduction
developed a hierarchical two-stage dynamic and static customized bus network service optimization model based on the fundamentals of the branch-and-bound method, and developed an approximate accurate solution algorithm to optimize the passenger service level of the original customized bus routes [38]. [PERSON] et al. used the Bertsimas-Sim robust optimization theory to transform a model containing unknown parameters to solve a customized bus route planning problem containing multiple yards and stations, saving 42.11% of computation time compared with the general genetic algorithm [39]. [PERSON] et al. decomposed the customized bus route design into a vehicle route problem with a time window and a passenger-vehicle bilateral matching problem, designing an H-R bilateral matching algorithm to solve the problem that achieved a passenger service rate of 88.5% in a real example in Tianjin [40]. The method also had a good performance in the problem scenario of [PERSON] et al. [41]. [PERSON] established a three-layer logit model of passenger travel mode choice for the product characteristics and operation modes of customized buses, used the improved DBSCAN algorithm to complete the demand clustering, and finally established a path optimization model for customized buses that includes random user equilibrium and a time window [42]. In summary, scholars have achieved rich results in the field of bus operation optimization and formed a relatively perfect theoretical system. However, the existing research still has room for further optimization in the following aspects:  
1. Some scholars simply used one of the strategies of bus-holding, stop-skipping, or signal control to avoid bus crosstown or late buses, which is for the control of two adjacent buses, without considering that changes in the status of adjacent buses will have an impact on the overall operation of the bus line, so the final scheduling program has a large optimization space. 2. Research on the real-time dynamic adjustment of bus running routes is limited to demand-responsive buses, and has not been applied to the operation optimization of conventional buses. There is a lack of research on the possible impacts of the localized and limited dynamic adjustment optimization of conventional bus running routes, which is not conducive to releasing the full potential of the bus system or improving the refinement level of bus operations. Based on the existing research, this study aims to comprehensively quantify and analyze the operation process based on urban traffic multi-source data for single-line buses, determine feasible vehicle operation control strategies, and establish a dynamic synergistic optimization model based on a combined strategy to optimize and improve bus operation and intelligent regulation and explore feasible evolutionary algorithms and solution schemes. The main research contents of this paper are as follows:  
1. From the perspective of overall system optimization, a dynamic control strategy for public transportation is proposed by combining bus-skipping control and operation path optimization. 2. With the dual optimization objectives of minimizing bus operation costs and minimizing passenger travel costs, the optimization objectives and constraints are clarified and a dynamic optimization model for single-line bus operations is established. 3. A hybrid heuristic algorithm based on simulated annealing principles and the genetic algorithm is designed for the model characteristics, based on which, a two-stage optimization model-solving scheme is proposed.