交通运输系统工程与信息 ›› 2023, Vol. 23 ›› Issue (2): 22-31.DOI: 10.16097/j.cnki.1009-6744.2023.02.003

• 综合交通运输体系论坛 • 上一篇    下一篇

出行即服务环境下个体出行链碳足迹监测与评估

李文翔1,程佳楠1,刘向龙*2,3,穆凯2,3,蔡近近1,刘魏巍1   

  1. 1. 上海理工大学,管理学院,上海 200093;2. 交通运输部科学研究院,北京 100029; 3. 城市公共交通智能化交通运输行业重点实验室,北京 100029
  • 收稿日期:2023-01-27 修回日期:2023-03-01 接受日期:2023-03-10 出版日期:2023-04-25 发布日期:2023-04-18
  • 作者简介:李文翔(1992- ),男,江西赣州人,副教授,博士
  • 基金资助:
    国家自然科学基金(52002244);上海市晨光计划(20CG55);城市公共交通智能化交通运输行业重点实验室开放课题(2021-APTS-01)

Monitoring and Assessing Carbon Footprint of Individual Trip Chain in Environment of Mobility as a Service

LI Wen-xiang1, CHENG Jia-nan1, LIU Xiang-long*2,3, MU Kai2,3, CAI Jin-jin1, LIU Wei-wei1   

  1. 1. Business School, University of Shanghai for Science and Technology, Shanghai 200093, China; 2. China Academy of Transportation Sciences, Beijing 100029, China; 3. Key Laboratory of Advanced Public Transportation Science, Beijing 100029, China
  • Received:2023-01-27 Revised:2023-03-01 Accepted:2023-03-10 Online:2023-04-25 Published:2023-04-18
  • Supported by:
    National Natural Science Foundation of China (52002244);Shanghai Chenguang Program (20CG55);Open Funds for Key Laboratory of Advanced Public Transportation Science (2021-APTS-01)

摘要: 在我国“碳达峰”“碳中和”战略目标下,科学准确地监测和评估个体出行碳足迹是推动城市交通低碳转型的基础,但同时也面临着巨大的挑战。因此,借助出行即服务(Mobility as a Service, MaaS)平台的数据开放与共享,旨在提出MaaS环境下城市个体出行链碳足迹监测与评估方法。首先,设计基于MaaS平台的城市交通碳源监测指标体系,实现对个体出行链多模式交通特征的提取与融合;然后,分别建立针对机动车和轨道交通出行的碳排放计算模型,计算不同交通方式的出行段碳排放,累加得到个体全链出行碳足迹;最后,以小汽车出行为基准线情景,评估个体出行链碳减排量。对在北京市采集的1865条出行链数据进行实例分析,结果显示:以小汽车、常规公交、轨道交通及非机动车为主导的出行链的平均人公里碳排放量分别为 0.2380,0.0310,0.0390,0.0017 kg · pkm-1,相对基准线的平均人公里减碳量分别为 0.029,0.220,0.230,0.280 kg·pkm-1;出行链人公里碳减排量与出行链中绿色出行比例正相关;对MaaS平台车辆电动化可使得减碳效益提高52.5%。

关键词: 城市交通, 碳足迹监测, 数据分析, 出行即服务, 出行链

Abstract: Under China's strategic goal of "carbon peaking and carbon neutrality", scientific and accurate monitoring and evaluation of the carbon footprint of individual travel is the basis for promoting the low-carbon transformation of urban transportation, but it also faces great challenges. Therefore, based on the data openness and sharing of the Mobility as a Service (MaaS) platform, a carbon footprint monitoring and evaluation method for urban individual trip chains in the MaaS environment is proposed. Firstly, we designed an urban transportation carbon source monitoring index system based on the MaaS platform and realized the extraction and integration of multi-modal transportation characteristics of users' individual trip chains. Then, we established the carbon emission calculation models for trips by private motor vehicles and rail transit, respectively, and the carbon emissions of different transportation modes are calculated and then added up to obtain the carbon footprint of individual complete trip chains. Finally, the carbon emission reduction of the individual trip chain was evaluated by using the baseline scenario of car trips. The case analysis of 8424 travel sections collected in Beijing shows that the average person-kilometer carbon emissions of the trip chain dominated by motor vehicles, buses, rail, and non-motor vehicles are 0.238 kg · pkm- 1 , 0.031 kg · pkm- 1 ,0.039 kg · pkm-1 , and 0.0017 kg · pkm-1 , respectively, and the average person-kilometer carbon reduction to the baseline is 0.029 kg · pkm-1 , 0.22 kg · pkm-1 , 0.23 kg · pkm-1 , and 0.28 kg · pkm-1 , respectively. The carbon emission reduction of person-kilometers in the trip chain is positively correlated with the proportion of green travel in the trip chain. Electrifying vehicles on MaaS platforms could increase carbon reduction benefits by 52.5%.

Key words: urban traffic, carbon footprint monitoring, data analysis, mobility as a service, trip chain

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