###### Abstract  
Optimizing travel routes in urban transportation networks is essential for enhancing traffic efficiency in major cities. This study focuses on optimizing the combined subway and taxi travel routes from Beijing Union University to Beijing South Railway Station, a critical route for students and staff in Beijing. To address this, we propose three genetic algorithms: the Standard Genetic Algorithm (SGA), the Multipoint Crossover and Elitist Selection Genetic Algorithm (MPEGA), and the Improved Dynamic Crossover and Mutation Rate Genetic Algorithm (IDCMGA). Experimental results indicate that MPEG reduces the mean travel cost by 15.21%, the variance by 81.72%, and the standard deviation by 57.25% compared to SGA. Additionally, IDCMGA reduces the average by 1.76%, the variance by 63.16%, and the standard deviation by 39.30% compared to MPEG. The improved IDCMGA algorithm demonstrates significant advantages over the other two algorithms in terms of global optimization capability, convergence speed, and stability, and is more adept at adapting to new traffic conditions to identify the optimal route. Optimizing this route not only reduces commuting time and costs but also alleviates traffic congestion, thereby enhancing the overall efficiency of the urban transportation system. genetic algorithm; path optimization; dynamic crossover rate; dynamic mutation rate; Beijing South railway station +
Footnote †: journal:disaster resilience of transport systems. Transportation systems, as a cornerstone of sustainable development, should be designed to positively impact economic, environmental, and social sustainability. [PERSON] [4] proposed optimizing a transport network using a multi-objective robust fuzzy planning approach, considering passenger dissatisfaction due to extra walking and waiting as an indicator of social sustainability. The results affirmed the superiority of the approach under uncertain demand. Recently, the study of path optimization in uncertain environments has garnered significant attention. Under uncertainty, the objective of path optimization is to identify the optimal path that satisfies specific demands as much as possible. [PERSON] [5] introduced the demand default (RV) index to quantify risk and utilized the Benders decomposition algorithm to identify the optimal path solution. The results indicated that the method performs well in practical applications. [PERSON] [6] proposed a path optimization method based on Fitness-Ant Colony Optimization (FACO). The results demonstrated that the FACO method significantly extends driving distance and reduces energy consumption, thereby enhancing the operational efficiency of electric vehicles. [PERSON] [7] proposed a priority coding and heuristic operator-based particle representation method to mitigate loop formation during path construction. The method can identify the optimal path with a high success rate across various network topologies. [PERSON] [8] proposed a multi-objective ant colony optimization (ACO) algorithm for addressing the bi-objective shortest path problem. The algorithm generates high-quality non-dominated solutions and reduces computational time when addressing large-scale bi-objective shortest path problems. [PERSON] [9] proposed achieving multi-target tracking by detecting targets in each frame and linking detection results across frames to avoid issues associated with single-frame detection failures. The linking step is reformulated as a constrained flow optimization problem, converting it into a convex optimization problem, and solved using the K shortest path algorithm, demonstrating superior performance in diverse environments. Since their inception, genetic algorithms (GAs) have been recognized as an effective and efficient approach for solving combinatorial optimization problems. However, Standard Genetic Algorithms (SGAs) often encounter challenges such as premature convergence and local optimization when addressing complex real-world problems.