摘要
针对旅客个性化出行需求和机场快速疏解要求,在对陆侧交通各出行方式固定分配的基础上,提出了一种基于二次诱导的群体空港旅客出行推荐方法,为定制化旅客服务提供算法支撑;基于旅客原始数据,结合粗糙集理论进行特征属性的知识约简,提高算法性能;运用改进贝叶斯分类算法进行基于旅客独立特征概率计算的出行方式推荐度量化,生成基于一次诱导的旅客出行推荐序列;面向机场陆侧各出行方式运力固定分配的约束,将旅客出行推荐序列输入基于改进非支配排序遗传算法(NSGA-II)的旅客二次诱导出行推荐模型中,进行运力与旅客流的深度匹配,对旅客出行推荐结果进行再次优化;基于普适性原则,使用小规模(100人次)和大规模(1000人次)旅客样本进行模型验证。分析结果表明:在不同规模旅客流输入情况下均能得到良好结果,小规模样本下旅客出行方式推荐正确率为77.41%,大规模样本下旅客出行方式推荐正确率为79.62%;经过二次诱导后,旅客流出行推荐分布与运力间的匹配度相比真实出行以及一次诱导分布皆有巨大提升。在旅客流与运力高度匹配的基础上实现了旅客出行偏好需求,算法性能良好,为改善枢纽机场客流疏解能力提供了一种切实可行的方法。
In view of the personalized travel needs of passengers and the requirements of rapid airport evacuation,a travel recommendation method for group airport passengers based on the secondary induction was proposed on the basis of the fixed allocation of each travel mode of landside transportation,so as to provide algorithmic support for customized passenger services.Based on the original passenger data,and combined with the rough set theory,the knowledge reduction of feature attributes was carried out to improve the performance of the algorithm.The improved Bayesian classification algorithm was used to quantify the travel mode recommendation degree based on the calculation of the independent feature probability of passengers,and the passenger travel recommendation sequence based on the primary induction was generated.In view of the constraint of fixed capacity allocation of each travel mode on the landside of the airport,the passenger travel recommendation sequence was input into the secondary-induced travel recommendation model of passengers based on the improved non-dominated sorting genetic algorithm(NSGA-Ⅱ)to deeply match the transport capacity and passenger flow,and the passenger travel recommendation results were optimized again.Based on the principle of universality,the small-scale(100 people)and large-scale(1000 people)passenger samples were used for model validation.Analysis results show that good results can be obtained under the inputs of passenger flows with different scales.The correct rate of passenger travel mode recommendation in the small-scale sample is 77.41%.Under the large-scale sample,the correct rate of passenger travel mode recommendation is 79.62%.After the secondary induction,the matching degree between the recommended travel distribution of passenger flow and the transport capacity greatly improves compared with the real travel and the primary induction distribution.On the basis of high matching between the passenger flow and the transport capacity,the passenger travel preference needs are realized.The algorithm has good performance and provides a practical method to improve the passenger flow evacuation of hub airports.5 tabs,8 figs,30 refs.
作者
柴琳果
芮涛
上官伟
蔡伯根
CHAI Lin-guo;RUI Tao;SHANGGUAN Wei;CAI Bai-gen(School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China;State Key Laboratory of Advanced Rail Autonomous Operation,Beijing Jiaotong University,Beijing 100044,China;Wuhan Branch of China Railway Major Bridge Reconnaissance and Design Institute Co.,Ltd.,Wuhan 430073,Hubei,China)
出处
《交通运输工程学报》
EI
CSCD
北大核心
2023年第6期301-313,共13页
Journal of Traffic and Transportation Engineering
基金
国家重点研发计划(2018YFB1601203)。
关键词
旅客出行推荐
改进非支配排序遗传算法
二次诱导
属性约简
旅客流与运力匹配
passenger travel recommendation
improved non-dominated sorting genetic algorithm
secondary induction
attribute reduction
passenger flow and transport capacity matching
作者简介
柴琳果(1988-),男,湖北荆门人,北京交通大学副教授,工学博士,从事交通信息工程及控制研究;通讯作者:上官伟(1979-),男,陕西咸阳人,北京交通大学教授,工学博士。
