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作者:

Liu, Jianmiao (Liu, Jianmiao.) | Li, Junyi (Li, Junyi.) | Chen, Yong (Chen, Yong.) | Lian, Song (Lian, Song.) | Zeng, Jiaqi (Zeng, Jiaqi.) | Geng, Maosi (Geng, Maosi.) | Zheng, Sijing (Zheng, Sijing.) | Dong, Yinan (Dong, Yinan.) | He, Yan (He, Yan.) | Huang, Pei (Huang, Pei.) | Zhao, Zhijian (Zhao, Zhijian.) | Yan, Xiaoyu (Yan, Xiaoyu.) | Hu, Qinru (Hu, Qinru.) | Wang, Lei (Wang, Lei.) | Yang, Di (Yang, Di.) | Zhu, Zheng (Zhu, Zheng.) | Sun, Yilin (Sun, Yilin.) | Shang, Wenlong (Shang, Wenlong.) | Wang, Dianhai (Wang, Dianhai.) | Zhang, Lei (Zhang, Lei.) | Hu, Simon (Hu, Simon.) | Chen, Xiqun (Michael) (Chen, Xiqun (Michael).)

收录:

EI Scopus SCIE

摘要:

Passenger transportation is one of the primary sources of urban carbon emissions. Travel data acquisition and appropriate emission inventory availability make estimating high-resolution urban passenger transportation carbon emissions challenging. This paper aims to establish a method to estimate and analyze urban passenger transportation carbon emissions based on sparse trip trajectory data. First, a trip chain identification and reconstruction method is proposed to extract travelers' trip information from sparse trip trajectory data. Meanwhile, a city-scale trip sampling expansion method based on population and checkpoint data is proposed to estimate population movements. Second, the identified trip information (e.g., trip origin and destination, and travel modes) is used to calculate multimodal passenger transportation CO2 emissions based on a bottom-up CO2 emissions calculation approach. Third, we develop a multi-scale high-resolution transportation carbon emission calculation and monitoring platform and take the city of Hangzhou, one of China's leading cities, as our case study, with around 10 million daily trips data and a quarter million road links. Five modes of passenger transportation are identified, i.e., walking, cycling, buses, metro, and cars. Hourly carbon emissions are calculated and attributed to corresponding road links, which build up passenger transportation carbon emissions from road links to region and city levels. Results show that a typical working day's total passenger transportation CO2 emission is about 36,435 tonnes, equivalent to CO2 emissions from 4 million gallons of gasoline consumed. According to our analysis of the carbon emissions produced by approximately 40,000 km of roadways, urban expressways have the most hourly carbon emissions at 194 kg/(h.km). Moreover, potential applications of the developed methods and platform linking to smart mobility management (e.g., Mobility as a Service, MaaS) and how to work in tandem to support green transportation policies (e.g., green travel rewards and carbon credits in transportation) have been discussed.

关键词:

High -resolution CO 2 emissions Smart mobility Passenger transportation Sustainable transportation Urban carbon emissions Big data analytics

作者机构:

  • [ 1 ] [Liu, Jianmiao]Zhejiang Univ, Zhejiang Univ Univ Illinois Urbana Champaign ZJU U, Haining 314400, Peoples R China
  • [ 2 ] [Lian, Song]Zhejiang Univ, Zhejiang Univ Univ Illinois Urbana Champaign ZJU U, Haining 314400, Peoples R China
  • [ 3 ] [Yan, Xiaoyu]Zhejiang Univ, Zhejiang Univ Univ Illinois Urbana Champaign ZJU U, Haining 314400, Peoples R China
  • [ 4 ] [Hu, Qinru]Zhejiang Univ, Zhejiang Univ Univ Illinois Urbana Champaign ZJU U, Haining 314400, Peoples R China
  • [ 5 ] [Hu, Simon]Zhejiang Univ, Zhejiang Univ Univ Illinois Urbana Champaign ZJU U, Haining 314400, Peoples R China
  • [ 6 ] [Chen, Xiqun (Michael)]Zhejiang Univ, Zhejiang Univ Univ Illinois Urbana Champaign ZJU U, Haining 314400, Peoples R China
  • [ 7 ] [Li, Junyi]Zhejiang Univ, Coll Civil Engn & Architecture, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China
  • [ 8 ] [Chen, Yong]Zhejiang Univ, Coll Civil Engn & Architecture, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China
  • [ 9 ] [Zeng, Jiaqi]Zhejiang Univ, Coll Civil Engn & Architecture, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China
  • [ 10 ] [Dong, Yinan]Zhejiang Univ, Coll Civil Engn & Architecture, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China
  • [ 11 ] [He, Yan]Zhejiang Univ, Coll Civil Engn & Architecture, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China
  • [ 12 ] [Huang, Pei]Zhejiang Univ, Coll Civil Engn & Architecture, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China
  • [ 13 ] [Hu, Qinru]Zhejiang Univ, Coll Civil Engn & Architecture, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China
  • [ 14 ] [Zhu, Zheng]Zhejiang Univ, Coll Civil Engn & Architecture, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China
  • [ 15 ] [Sun, Yilin]Zhejiang Univ, Coll Civil Engn & Architecture, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China
  • [ 16 ] [Wang, Dianhai]Zhejiang Univ, Coll Civil Engn & Architecture, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China
  • [ 17 ] [Hu, Simon]Zhejiang Univ, Coll Civil Engn & Architecture, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China
  • [ 18 ] [Chen, Xiqun (Michael)]Zhejiang Univ, Coll Civil Engn & Architecture, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China
  • [ 19 ] [Geng, Maosi]Zhejiang Univ, Polytech Inst, Hangzhou, Peoples R China
  • [ 20 ] [Zheng, Sijing]Zhejiang Univ, Polytech Inst, Hangzhou, Peoples R China
  • [ 21 ] [Zhao, Zhijian]Zhejiang Univ, Polytech Inst, Hangzhou, Peoples R China
  • [ 22 ] [Sun, Yilin]Zhejiang Univ, Polytech Inst, Hangzhou, Peoples R China
  • [ 23 ] [Chen, Xiqun (Michael)]Zhejiang Univ, Polytech Inst, Hangzhou, Peoples R China
  • [ 24 ] [Wang, Lei]Alibaba Grp, Alibaba Cloud Intelligence, Hangzhou 310056, Peoples R China
  • [ 25 ] [Yang, Di]Alibaba Grp, Alibaba Cloud Intelligence, Hangzhou 310056, Peoples R China
  • [ 26 ] [Zhang, Lei]Alibaba Grp, Alibaba Cloud Intelligence, Hangzhou 310056, Peoples R China
  • [ 27 ] [Zhu, Zheng]Alibaba Zhejiang Univ Joint Res Inst Frontier Tech, Hangzhou 310007, Peoples R China
  • [ 28 ] [Sun, Yilin]Alibaba Zhejiang Univ Joint Res Inst Frontier Tech, Hangzhou 310007, Peoples R China
  • [ 29 ] [Wang, Dianhai]Alibaba Zhejiang Univ Joint Res Inst Frontier Tech, Hangzhou 310007, Peoples R China
  • [ 30 ] [Hu, Simon]Alibaba Zhejiang Univ Joint Res Inst Frontier Tech, Hangzhou 310007, Peoples R China
  • [ 31 ] [Chen, Xiqun (Michael)]Alibaba Zhejiang Univ Joint Res Inst Frontier Tech, Hangzhou 310007, Peoples R China
  • [ 32 ] [Shang, Wenlong]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China

通讯作者信息:

  • [Hu, Simon]Zhejiang Univ, Coll Civil Engn & Architecture, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China;;[Chen, Xiqun (Michael)]Zhejiang Univ, Coll Civil Engn & Architecture, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China;;

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来源 :

APPLIED ENERGY

ISSN: 0306-2619

年份: 2023

卷: 331

1 1 . 2 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:19

被引次数:

WoS核心集被引频次: 38

SCOPUS被引频次: 52

ESI高被引论文在榜: 0 展开所有

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