热带地理2025，Vol.45Issue(8)：1315-1328,14.DOI:10.13284/j.cnki.rddl.20250389
基于手机信令数据的居民出行碳排放测定框架构建及应用
An Individual-Level Framework for Measuring Urban Residents'Travel Carbon Emissions Based on Mobile Signaling Data:Construction and Application
摘要
Abstract
Accurately identifying the spatiotemporal characteristics of urban residents'travel carbon emissions is an important scientific issue in transportation geography and planning,and is also a prerequisite for formulating reasonable low-carbon transportation policies.Existing studies mostly use top-down methods for calculation,however,the lack of internal urban statistical data,difficulty in depicting the dynamic evolution and spatial distribution patterns of carbon emissions,and other factors,have restricted the further development of related research.Furthermore,problems such as small sample size,low accuracy,and the need to verify the effectiveness of bottom-up calculation methods have always been difficult to solve.Therefore,this study proposes a framework for measuring residents'travel carbon emissions using mobile phone signaling data to:1)Overcome the reliance on traditional statistical data,innovatively integrating travel survey logs with mobile phone signaling data,and effectively verifying the trajectory information.2)Extract factors influencing the travel mode choice,such as residents'social attributes,travel characteristics,public transportation service levels,and travel preferences,and uses the random forest algorithm to specifically identify five travel modes,balancing precision and accuracy.3)Comprehensively consider factors such as travel mode,distance,speed,vehicle energy consumption type,and passenger load rate to accurately reflect carbon emissions from individual residents'single trips.4)Aggregate by time,space,and population characteristics to multidimensionally reflect the carbon emission patterns of residents'travel.Taking Shenzhen as an example,based on the travel data of over 30 million residents,a technical application was conducted.The accuracy rate of individual travel mode identification was 77%.The aggregated carbon emission calculation results effectively revealed the highly concentrated distribution pattern of"two belts,three zones,and multiple points"and the functional spillover effect of the Shenzhen metropolitan area at the spatiotemporal level found that there were structural differentiations in traffic carbon emissions between working and non-working days,and between commuting peaks and general periods in different urban areas;at the population attribute level,the significant influence of age,gender,and other characteristics on residents'travel distances,travel modes,and travel carbon emissions were further confirmed.This framework is conducive to clearly and comprehensively revealing the spatiotemporal characteristics of urban residents'travel carbon emissions,providing new technology for high-precision monitoring of urban transportation carbon emissions and a basis for the formulation of urban transportation pollution reduction and emission reduction policies.关键词
手机信令数据/居民出行方式/碳排放/人群差异/低碳交通规划/深圳市Key words
mobile signaling data/residents'travel/carbon emissions/population differences/low-carbon transportation planning/Shenzhen分类
交通工程引用本文复制引用
赵鹏军,俞泽欣,赵虹剑,刘文洲,冯永恒,江世雄,陈睿..基于手机信令数据的居民出行碳排放测定框架构建及应用[J].热带地理,2025,45(8):1315-1328,14.基金项目
深圳市科技计划资助项目(JCYJ20220818100810024、KQTD20221101093604016、KJZD20230923114911022) （JCYJ20220818100810024、KQTD20221101093604016、KJZD20230923114911022）
