Optimization of Traffic System Based on Ant Colony Algorithm and Dijkstra's Algorithm

This study focuses on the urban transportation system by using the ant colony algorithm and Dijkstra's algorithm, aiming to comprehensively analyze the transportation system by combining various models. First, in the data processing stage, the median padding method and IQR outlier identification method are used to deal with the missing values and outliers, and the maximum normalization is used to standardize the AADT data in order to improve the data quality. Next, a weighted directed graph model is used to describe the transportation system, and Dijkstra's algorithm is used to evaluate the impact of key events on transportation. Next, the ACO algorithm is used to optimize the bus routes and construct the weighted directed graph. Finally, through the hierarchical analysis method (AHP) combined with the entropy method, the optimized public transportation system is determined as the optimal solution by considering multiple factors. The advantage of this study is that it provides a comprehensive and in-depth analysis for the optimization of the transportation system and a scientific basis for urban transportation planning and decisionmaking.