## 4 Conclusions  
This paper proposes an improved genetic algorithm (IDCMGA) that dynamically adjusts crossover and mutation rates to prevent premature convergence and enhance solution quality, aiming to optimize travel paths in urban transport networks. Through experiments and the analysis of combined subway and taxi travel paths from Beijing Union University to Beijing South Railway Station, the algorithm's significant advantages in global optimization capability, convergence speed, and solution quality are confirmed. Compared to SGA and MPEG, IDCMGA demonstrates superior adaptability and robustness. Experimental results show that IDCMGA can enhance urban traffic management by reducing commuters' travel time and costs, thereby improving traffic flow and alleviating congestion. Its strong performance under complex traffic conditions suggests that IDCMGA can be integrated into real-time traffic management systems to enhance overall traffic flow and alleviate congestion. The algorithm is suitable for various urban scenarios, providing city planners and traffic management departments with a reliable tool for optimizing route planning and traffic control. IDCMGA represents a significant advancement in traffic network optimization by achieving more efficient and accurate global optimization. This innovative approach offers a new pathway for developing intelligent transportation systems, highlighting its potential impact on urban travel solutions. Future research should explore the application of IDCMGA in other complex traffic scenarios, such as multi-modal transportation networks and large-scale urban environments. Additionally, combining machine learning techniques with IDCMGA to further enhance its predictive capabilities and real-time decision-making is a promising direction. Investigating the scalability of IDCMGA in larger and more complex networks is crucial for expanding its applicability and ensuring its effectiveness in diverse urban environments.  
In summary, IDCMGA effectively finds the optimal solution in most cases, with its global optimization capability, stability, and adaptability significantly surpassing SGA and MPEG. These findings underscore the practical significance of IDCMGA in urban traffic  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SGA** & **MPEGA** & **IDCMGA** \\ \hline Average Value & 33.6157 & 29.12514286 & 28.87071429 \\ Variance & 16.71024884 & 2.274073803 & 0.728596939 \\ Standard Deviation & 4.087817125 & 1.50800325 & 0.8535789 \\ Running Time & 0.406 s & 0.410 s & 0.419 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance Comparison of Three Algorithms.  
management, providing more options for developing efficient, reliable, and intelligent traffic systems in the future.  
Conceptualization, Z.R.; methodology, B.D.; software, Y.L.; validation, Y.W. and P.J.; investigation, W.Y.; data curation, J.F.; writing--original draft preparation, B.D.; writing--review and editing, Z.R.; supervision, J.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Beijing Natural Science Foundation, grant number L221015.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.