Urban optimal traffic path planning algorithm based on hybrid ant colony algorithm

To enhance travel efficiency and minimize route distance, this study proposes an optimized urban traffic path planning method utilizing a hybrid ant colony optimization algorithm. Firstly, a grid map of urban transportation is constructed based on the Cartesian coordinate method. Secondly, calculate the distance traveled along the path and extract traffic features from the city grid map. Again, design the constraints for optimal urban transportation path planning from three aspects: path length constraints, time window constraints, and transfer frequency constraints. Finally, the ant colony algorithm and adaptive genetic algorithm are combined to form a hybrid ant colony algorithm, which plans pedestrian paths in a grid map and completes the optimal traffic path planning for the city. Through experiments, it has been proven that the planning delay of our method is always below 12ms, with a total waiting time of 92.80min and a total travel time of 584.15min.