###### Abstract
To address these challenges, researchers have developed various improved algorithms. [PERSON] [10] employed a genetic algorithm to compute the fitness function value in bus network optimization. This method evaluates each network member through a multi-criteria analysis to generate a new population and designs heuristics that meet the demand and provide an optimal bus network. [PERSON] [11] proposed an optimization method incorporating a network equilibrium model to optimize signal green times and cycle times using a genetic algorithm. The results demonstrated that the signal timings optimized by the genetic algorithm significantly outperformed methods that did not consider rerouting in highly congested networks. [PERSON] [12] proposed a detailed formulation for optimizing bus priority at the network level and used heuristics to find the optimal solution. A two-layer planning approach was employed to manage large networks using genetic algorithms. The results indicated that the method successfully considered the interests of all stakeholders, and the introduction of bus lanes proved to be effective. The implementation of parallel genetic algorithms significantly reduced computation time. [PERSON] [13] proposed a method for scheduling bus vehicles to mitigate traffic congestion in uncertain environments. Experiments demonstrated that this method can dynamically generate scheduling plans and enhance service quality. Additionally, a multi-objective genetic algorithm was introduced to optimize bus routes and schedules. [PERSON] et al. [14] introduced a column generation method based on a genetic algorithm (GA-CG) to solve the multi-depot electric vehicle scheduling problem (MD-EVSP) in public transportation. The experimental results demonstrated that compared to the branch and price (BP) algorithm and manual scheduling schemes, GA-CG effectively solves the problem and is approximately 40 times faster than the BP algorithm. [PERSON] [15] proposed a data-driven method to optimize bus timetables, constructing a bi-objective optimization model to minimize passenger waiting times and bus company departure times. Utilizing improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to quickly search for Pareto optimal solutions, the method was validated on a bus line in Beijing. The results demonstrated that this method can quickly provide high-quality and reasonable timetable schemes compared to traditional single-objective optimization methods. [PERSON] [16] integrated fast-running machine learning algorithms and reliable genetic algorithms (GAs) into a single optimization framework to optimize signal timings under non-recurrent traffic incidents. The results indicated that the new BGA-ML algorithm is significantly faster than the original GA algorithm in handling incident conditions and can reduce total travel time by nearly half. These studies illustrate the extensive application of genetic algorithms in traffic optimization. Genetic algorithms (GAs) exhibit robustness and flexibility in exploring wide search spaces and gradually converging to optimal or near-optimal solutions, making them particularly suitable for this problem. However, practical applications often face issues such as slow convergence and premature stagnation. Slow convergence can lead to wasted computational resources and poor user experience, while premature stagnation can cause the algorithm to become trapped in local optima, resulting in poor solution stability and limited room for improvement. These issues can significantly impact the efficiency and effectiveness of the optimization process. Additionally, these studies possess some limitations. For example, most studies focus on single modes of transportation or specific issues, lacking comprehensive optimization for integrated transportation systems. While some studies illustrate the advantages of genetic algorithms in specific scenarios, their application in multi-modal, multi-objective traffic optimization still demands further exploration.