摘要
在多模式公交出行中,传统的路径规划方案已无法满足出行者日益增长的出行需求.为提供基于出行者多种出行需求的个性化路径规划方案,通过IC卡刷卡数据模拟公交时刻表,建立基于模拟时刻表的多模式公交路网模型;采用动态阈值化法建立个性化出行需求评价值模型;设计深度优先搜索-遗传算法(depth first searchgenetic algorithm,GA-DFS),并基于此组合算法提出初始种群产生策略和两点变异方法;最后,假设了3种不同出行需求的出行场景,将某市区的多模式公交路网数据应用于模型和求解算法中,并与使用较广的模拟退火-遗传算法(simulated annealing-genetic algorithm,GA-SA)进行对比分析.仿真结果表明:所提出的算法与模拟退火-遗传算法相比,平均迭代次数减少了42%,寻优能力提高了50%,并且可以提供基于乘客多种出行需求的路径规划方案.
Traditional route planning scheme cannot meet the increasing travel demand of travelers in the process of multimodal transportation.To provide personalized route planning scheme based on various travel demands of travelers,public transport timetable is simulated with the integrated circuit card data,and a multimodal transportation network modal is established based on simulated schedule.A dynamic thresholding method is used to establish the personalized travel demand evaluation value model.The depth first search-genetic algorithm(GADFS)is designed,and the initial population generation strategy and two-point mutation method based on this combination algorithm are proposed.Finally,three scenarios with different travel demands are assumed,the example data of a multimodal transportation network in an urban area is applied to the modal and the solution algorithm,comparing with the simulated annealing-genetic algorithm(GA-SA)which is widely used.The results show that compared with GA-SA,the proposed algorithm reduces the average number of iterations by 42%,improves the optimization ability by 50%and provides a route planning scheme based on multiple travel demands of passengers.
作者
王志建
刘士杰
周锦瑶
孙健
WANG Zhijian;LIU Shijie;ZHOU Jinyao;SUN Jian(School of Electrical and Control Engineering,North China University of Technology,Beijing 100144,China;Beijing Aerospace Measurement&Control Technology Co.,Ltd.,Beijing 100041,China)
出处
《西南交通大学学报》
EI
CSCD
北大核心
2022年第6期1319-1325,1333,共8页
Journal of Southwest Jiaotong University
基金
国家自然科学基金(72071003)
北京市教育委员会科研计划(110052971921/023)。
关键词
城市交通
路径选择
遗传算法
深度优先搜索
多模式公交
多出行需求
urban transportation
route choice
genetic algorithm
depth first search
multimodal public transportation
multiple travel demands
