摘要
手机信令数据具有覆盖范围广、获取成本低、时空精度较高、稳定实时追踪等优势,能够有效识别大规模人群的空间活动和出行特征,已成为应用最广泛的交通大数据类型之一。文章在手机信令数据的分类和特征基础上,总结了其在职住空间关系和交通出行行为研究中的技术应用,随后结合上述应用成果和已有文献对其在交通碳排放研究中的应用潜力和场景进行了探讨,最后总结了手机信令数据在职住空间、出行行为和交通碳排放研究中的应用框架、应用机遇与挑战以及未来研究内容与技术创新方向。目前,手机信令数据在职住空间领域中的应用包括职住地识别、职住关系和通勤网络特征及其影响因素解析,在出行行为领域中的应用包括驻留-出行识别、出行方式和路径识别,以及人群移动普适规律解析。以上技术应用能够有效服务交通碳排放领域研究,为交通碳排放测算以及城市空间结构、居民出行行为对交通碳排放的影响研究奠定了基础。未来,相关研究应进一步关注长时序动态追踪、大范围对比分析以及人口和交通新现象研究,并注重多源数据的融合、传统方法与机器学习的结合以及数字孪生模型的构建。
The rapid development of information technology has triggered an explosion of data,marking the era of big data.A wide range of transportation big data has been used in urban space and travel behavior studies since the beginning of this century.Mobile phone signaling data in particular have many advantages:they have prevalent spatial and temporal coverage,high tracking stability,satisfactory resolution,and low cost.The description of urban phenomena and the analysis of their forming mechanisms using mobile phone signaling data are thoroughly studied by previous research.The next course of action is to tackle specific urban problems.This study summarizes the application progress of mobile phone signaling data in job-housing relationships and travel behavior studies,discusses the application prospects of mobile phone signaling data in transportation carbon emissions research based on past applications and the existing literature on low-carbon transportation,and proposes a research framework and several future directions for studies using mobile phone big data to examine job-housing relationships,travel behavior,and transportation carbon emissions.We first provide a brief introduction to the features of mobile phone signaling data in comparison with other commonly used data types,including their type,content,and spatial-temporal resolution.We then review the existing applications in job housing and travel research.Regarding the jobs-housing relationship,prior studies employ mobile phone signaling data to detect the spatial distribution of workplaces and residences of urban dwellers,analyze jobshousing relationship features and urban spatial structure characteristics,and examine the factors influencing jobshousing relationships.Regarding travel behavior,studies employ mobile phone signaling data to identify stays and trips,infer trip modes,detect trip routes,and explore the universal laws of human mobility.Next,we also discuss how mobile phone signaling data can be applied to transportation carbon emissions research.I
作者
高瑜堃
赵鹏军
Gao Yukun;Zhao Pengjun(College of Urban and Environmental Sciences,Peking University,Beijing 100871,China;School of Urban Planning and Design,Peking University Shenzhen Graduate School,Shenzhen 518055,China)
出处
《热带地理》
CSCD
北大核心
2024年第5期877-890,共14页
Tropical Geography
基金
国家自然科学基金项目(41925003、42130402)
深圳市科技计划资助项目(JCYJ20220818100810024)。
关键词
手机信令数据
职住空间关系
交通出行行为
交通碳排放
mobile phone signaling data
jobs-housing relationship
travel behavior
transportation carbon emissions
