The Impact of Path Planning Model Based on Improved Ant Colony Optimization Algorithm on Green Traffic Management

Abstract: In response to the demand for green city construction, low-carbon travel standards have been further implemented. This research focuses on intelligent transportation management and designs path planning algorithms. Firstly, the basic model of the proposed ant colony optimization algorithm was constructed. In response to the poor convergence of traditional algorithms, a rollback strategy was introduced to optimize the model taboo table. Subsequently, in response to the dynamic obstacle avoidance problem in practical applications, the optimized A* algorithm was studied and applied to global path planning. The improved ant colony algorithm was applied to local obstacle avoidance planning, further enhancing the accuracy and practicality of the algorithm. In simulation analysis, facing more complex simulation environments, this research method could better achieve obstacle avoidance path planning. The average number of search nodes decreased by 6, the average search time decreased by 4.11%, and the average path length decreased by 22.07%. In summary, the ant colony optimization algorithm designed through research is more suitable for path planning needs in different scenarios, with the best overall performance. It can plan the shortest driving path while ensuring precise obstacle avoidance, helping to achieve green traffic management.

Huan Yu. “The Impact of Path Planning Model Based on Improved Ant Colony Optimization Algorithm on Green Traffic Management”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.6 (2024). http://dx.doi.org/10.14569/IJACSA.2024.01506109

@article{Yu2024,
title = {The Impact of Path Planning Model Based on Improved Ant Colony Optimization Algorithm on Green Traffic Management},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01506109},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01506109},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {6},
author = {Huan Yu}
}