摘要
针对公交线路资源利用率偏低的问题,基于乘客需求响应对公交路径网络建立数学模型,包括建立路径模型的目标函数和约束函数。采用K-means聚类算法实现公交网络站点的规划,同时将ESGA遗传算法应用于路径规划解算中,实现对公交路径的优化。在仿真实验中将该方法与典型的FWSGA遗传算法相比较,实验结果表明,该方法具有更快的收敛速度和更优的搜索结果。
In order to solve the problem of low utilization rate of bus route resources,a mathematical model of bus route network is established by using passenger demand response. Including the establishment of the objective function and constraint function of the path model,the K-means clustering algorithm is used to realize the planning of the bus network stations,and the ESGA genetic algorithm is applied to the path planning solution to realize the optimization of the bus path. The simulation results show that this method has faster convergence speed and better search results compared with the typical FWSGA genetic algorithm.
作者
武可心
WU Kexin(School of Traffic and Transportation,Xi’an Traffic Engineering Institute,Xi’an 710300,China)
出处
《电子设计工程》
2022年第19期35-38,43,共5页
Electronic Design Engineering
基金
陕西省教育厅科学研究计划项目资助(22JK0447)。
关键词
公交网络
遗传算法
神经网络
优质群体
仿真
public transportation network
genetic algorithm
neural network
high quality group
simulation
作者简介
武可心(1991—),女,陕西西安人,硕士研究生,讲师。研究方向:智能交通运输规划与控制。
