旅游城镇路网多目标优化模型及算法设计

Multi-objective optimization model and algorithm design of road network in tourist town

为缓解旅游城镇的交通拥堵问题,提出将宏观交通仿真软件与启发式算法相融合进行路网优化设计.基于旅游城镇的交通流特点,将旅游城镇路网优化问题抽象为多目标优化模型进行研究.以路网中各条路段饱和度系统最优及投资成本最低为目标,建立多目标优化模型,寻求交通拥堵治理与道路资源投入博弈中的最优平衡点.运用TransCAD仿真软件,采用用户均衡模型对初始路网进行流量分配,基于遗传算法进行模型算法设计,编写Matlab算法程序求解模型.通过多次算法实验并对比分析实验结果,确定目标函数的最佳权重.算例分析结果表明,路网中所有路段饱和度的总和由16.70下降至11.12,平均值由0.93下降至0.62,且各优化后的路段饱和度稳定在0.5~0.8的合理范围内,路网整体饱和度明显降低且更加均衡,投资成本合理.研究结果对新型旅游城镇的路网规划具有一定指导意义.

To alleviate traffic congestion in tourist towns,we propose a method to optimize the road network design by combining the macro-traffic simulation software with the heuristic algorithm.Based on the characteristics of traffic flow in tourist towns,the problem of optimal design of tourism urban road network is transformed into a multi-objective optimization model.To seek the optimal balance point between traffic congestion management and road resource investment,a multi-objective optimization model is established to optimize the saturation system of each road section and minimize the investment cost.We use TransCAD simulation software and the user equilibrium model to distribute the traffic flow of the initial road network,then,design the model algorithm based on genetic algorithm,and at last,design Matlab algorithm program to solve the model.The optimal weight of the objective function is determined by multiple algorithm experiments and comparative analysis of the experimental results.The results of the numerical example show that the total saturation value of all sections in the road network decreases from 16.70 to 11.12,and the average saturation value of all sections in the road network decreases from 0.93 to 0.62.The saturation of optimized sections is stable in a reasonable range from 0.5 to 0.8.The overall road network saturation is significantly reduced and becomes more balanced,moreover,the cost of investment is more reasonable.Therefore,results of the study have certain guiding significance for the road network planning of new tourist towns.