摘要
系统分析了居民出行调查的误差成因,简要介绍了传统加权扩样模型并分析了其局限性,特别是对沉默出行需求揭示不足的缺点。结合手机信令数据用于出行行为研究的优势及特点,提出了利用手机信令数据进行居民出行调查扩样及对挖掘沉默出行需求的方法。设计了包含5个主要步骤的居民出行调查样本数据、交通运行监测数据和手机信令数据相结合的扩样模型结构。以驻点分类技术为基础,建立了基于手机信令数据的出行活动分布模型。在获取长周期手机信令数据推断人口职住分布的基础上,基于居民出行调查RP(revealed preference)数据对交通方式划分模型参数进行了标定。从出行目的组成、时间分布和出行距离分布3个方面,对比分析了调查样本、传统加权扩样模型和新模型的计算结果的差异,结果表明新模型在揭示因瞒报、漏报而产生的沉默出行需求方面作用显著。
In this paper,the causes of errors in household travel surveys were analyzed,the traditional weighted sample expansion model was introduced,and its limitations,especially the lack of unreported trip records were analyzed.Combining the advantages and characteristics of the research method using cellphone data on travel behaviors,a novel model of using cellphone data to expand the sample of household travel surveys and mining unreported trip records was proposed.A sample expansion model was designed including five steps,combining the household travel survey data,traffic operation monitoring data,and cellphone data.Based on the stationary point classification technology,a travel behavior distribution model based on cellphone data was established.The parameters of the model split model were calibrated based on the revealed preference data of household travel surveys.The difference between the results of the survey sample,the traditional weighted sample expansion model,and the proposed model was analyzed from the aspects of travel purpose composition,time of day,and travel distance distribution.The results show that the model proposed reveals the unreported trip record caused by false reports and omissions.
作者
陈小鸿
陈先龙
李彩霞
陈嘉超
CHEN Xiaohong;CHEN Xianlong;LI Caixia;CHEN Jiachao(Key Laboratory of Road and Traffic Engineering of the Ministry of Education,Tongji University,Shanghai 201804,China;Information and Modelling Department,Guangzhou Transport Planning and Research Institute,Guangzhou 510030,China)
出处
《同济大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2021年第1期86-96,共11页
Journal of Tongji University:Natural Science
基金
国家自然科学基金重点项目(71734004)。
关键词
居民出行调查
扩样
加权扩样
手机信令数据
沉默出行需求
驻点分类
household travel survey
sample expansion
weighted sample expansion
cellphone data
unreported trip record
stationary point classification
