## 3 Results  
This study compares the performance of three distinct genetic algorithms in solving the shortest path problem from Beijing Union University to Beijing South Railway Station, each tested over 100 runs: the Standard Genetic Algorithm (SGA), the Multipoint Crossover plus Elite Strategy Selection Genetic Algorithm (MPEGA), and the Improved Multipoint Crossover and Elite Strategy Selection Combined with Dynamic Crossover and Mutation Rate Genetic Algorithm (IDCMGA). Experimental results demonstrate that IDCMGA outperforms the other two algorithms in terms of the fitness score, mean value, variance, and standard deviation. SGA performs averagely in its ability to find the optimal solution, with slower convergence and higher values of variance and standard deviation, indicating a high degree of volatility and instability, as shown in Figure 5a. The results of SGA after 100 runs, shown in Figure 5b, reveal significant fluctuations in its performance, highlighting the algorithm's instability. The frequent fluctuations indicate that SGA has difficulty consistently finding the optimal path. This instability highlights the algorithm's limited ability to reliably identify optimal paths in complex traffic networks, underscoring the need for more advanced and stable optimization techniques. In contrast, the results of MPEG after 100 runs show significant improvement. With the introduction of multipoint crossover and elite strategy selection, the optimization efficiency and path quality of the algorithm are significantly improved, as shown in Figure 6a, and its volatility and stability are further improved, as shown in Figure 6b. However, although MPEG outperforms SGA in terms of overall performance, it still suffers from some volatility and instability, indicating room for improvement in seeking the global optimal solution. By dynamically adjusting the crossover rate and mutation rate, IDCMGA exhibits small fluctuations in the curve when fitness decreases, as illustrated in Figure 7a. Through dynamic adjustment, the algorithm maintains high diversity during the early exploration phase. As depicted in the figure, significant fluctuations in fitness values during the initial generations indicate that the algorithm is exploring different regions of the solution space. In subsequent iterations, the fitness values stabilize, demonstrating that the algorithm is gradually converging to the optimal solution. The dynamic adjustment of the crossover rate and mutation rate significantly improves the best fitness score of IDCMGA after 100 runs, as depicted in Figure 7b. IDCMGA not only maintains high performance but also significantly enhances the global optimization capability and adaptability of the algorithm. While improving convergence speed, its stability and consistency are also significantly enhanced, further verifying the effectiveness of the dynamic adjustment strategy in enhancing the overall performance of the genetic algorithm. The experimental results after 100 runs of each of the three algorithms are shown in Table 3. IDCMGA outperforms the other two algorithms in terms of the mean, variance, and standard deviation. However, IDCMGA and MPEG take slightly longer to run than SGA because MPEG incorporates a multipoint crossover strategy and an elite selection module, while IDCMGA also adds dynamic crossover and mutation rate strategies. In conclusion, the experimental results demonstrate that IDCMGA effectively finds the optimal solution in most cases, significantly surpassing SGA and MPEG in terms of global optimization capability, stability, and adaptability. The practical implications of these findings are substantial. For instance, IDCMGA's enhanced performance can be directly applied to optimizing urban transportation networks, resulting in reduced travel times and costs for commuters. Furthermore, its robustness and adaptability make it ideal for dynamic and complex traffic conditions, presenting potential applications in real-time traffic management systems.