Dynamic Urban Traffic Network Allocation Design Based on Improved Mayfly Algorithm

With the continuous advancement of technology, the number of vehicles in cities is increasing, and traffic congestion is becoming more common. To enhance the efficiency and effectiveness of urban transportation network allocation, a constraint method is introduced to improve the Nondominated Sorting Genetic Algorithm III. By dynamically adjusting and optimizing the mayfly algorithm, the two are integrated to ultimately construct a dynamic urban transportation network allocation method. Combining the advantages of both algorithms, the population and optimization objectives are dynamically adjusted to achieve multiobjective optimization, including reducing travel time, congestion, and environmental pollution. Through comparative experimental testing of performance, the findings denote that the proposed dynamic urban transportation network allocation method has the highest super volume, which is 0.758. The generational distance, inverted generational distance, and spacing are the lowest, at 0.156, 0.179, and 0.113, respectively. The experimental findings indicate that the proposed allocation method improves the efficiency and effectiveness of urban transportation network allocation through precise optimization of transportation network allocation and has strong applicability. Research provides scientific decision support for transportation planning and management by effectively finding a balance between multiple objectives in dynamic urban transportation network allocation.