IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v331y2023ics0306261922016646.html
   My bibliography  Save this article

Multi-scale urban passenger transportation CO2 emission calculation platform for smart mobility management

Author

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

Abstract

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.

Suggested Citation

  • Liu, Jianmiao & Li, Junyi & Chen, Yong & Lian, Song & Zeng, Jiaqi & Geng, Maosi & Zheng, Sijing & Dong, Yinan & He, Yan & Huang, Pei & Zhao, Zhijian & Yan, Xiaoyu & Hu, Qinru & Wang, Lei & Yang, Di & , 2023. "Multi-scale urban passenger transportation CO2 emission calculation platform for smart mobility management," Applied Energy, Elsevier, vol. 331(C).
    • Handle: RePEc:eee:appene:v:331:y:2023:i:c:s0306261922016646
    DOI: 10.1016/j.apenergy.2022.120407
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922016646
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.120407?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Haoran & Chen, Jinyu & Li, Wenjing & Song, Xuan & Shibasaki, Ryosuke, 2020. "Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential," Applied Energy, Elsevier, vol. 269(C).
    2. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Li, Jiangtao & Yang, Zhenzhen & Gao, Ziyou, 2022. "Identifying intercity freight trip ends of heavy trucks from GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    3. Bigazzi, Alexander, 2019. "Comparison of marginal and average emission factors for passenger transportation modes," Applied Energy, Elsevier, vol. 242(C), pages 1460-1466.
    4. Wen, Yifan & Wu, Ruoxi & Zhou, Zihang & Zhang, Shaojun & Yang, Shengge & Wallington, Timothy J. & Shen, Wei & Tan, Qinwen & Deng, Ye & Wu, Ye, 2022. "A data-driven method of traffic emissions mapping with land use random forest models," Applied Energy, Elsevier, vol. 305(C).
    5. Yang Zhou & Chao Yang & Rongrong Zhu, 2019. "Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai," Transportation Planning and Technology, Taylor & Francis Journals, vol. 42(8), pages 739-756, November.
    6. Zipeng Zhang & Ning Zhang, 2021. "A Novel Development Scheme of Mobility as a Service: Can It Provide a Sustainable Environment for China?," Sustainability, MDPI, vol. 13(8), pages 1-19, April.
    7. Li Shen & Peter R. Stopher, 2014. "Review of GPS Travel Survey and GPS Data-Processing Methods," Transport Reviews, Taylor & Francis Journals, vol. 34(3), pages 316-334, May.
    8. Markus Schläpfer & Lei Dong & Kevin O’Keeffe & Paolo Santi & Michael Szell & Hadrien Salat & Samuel Anklesaria & Mohammad Vazifeh & Carlo Ratti & Geoffrey B. West, 2021. "The universal visitation law of human mobility," Nature, Nature, vol. 593(7860), pages 522-527, May.
    9. Melinda Matyas & Maria Kamargianni, 2019. "The potential of mobility as a service bundles as a mobility management tool," Transportation, Springer, vol. 46(5), pages 1951-1968, October.
    10. Jia, Ning & Li, Liying & Ling, Shuai & Ma, Shoufeng & Yao, Wang, 2018. "Influence of attitudinal and low-carbon factors on behavioral intention of commuting mode choice – A cross-city study in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 108-118.
    11. Wang, Zhaohua & Liu, Wei, 2015. "Determinants of CO2 emissions from household daily travel in Beijing, China: Individual travel characteristic perspectives," Applied Energy, Elsevier, vol. 158(C), pages 292-299.
    12. Yi, Wenjing & Yan, Jie, 2020. "Energy consumption and emission influences from shared mobility in China: A national level annual data analysis," Applied Energy, Elsevier, vol. 277(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Qingqing & Shi, Jinbo & Ni, Kan & Wang, Ruohan & Zhang, Chongyi & Yang, Nan & Yang, Yifei & Shen, Yifan & Guo, Ru & Liao, Zhenliang, 2024. "A highly credible and efficient real-time carbon MRV + O system for delicacy management of distributed carbon abatement behaviors," Applied Energy, Elsevier, vol. 355(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin, Boqiang & Wang, Xia, 2021. "Does low-carbon travel intention really lead to actual low-carbon travel? Evidence from urban residents in China," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 743-756.
    2. Wu Li & Shengchuan Zhao & Jingwen Ma & Wenwen Qin, 2021. "Investigating Regional and Generational Heterogeneity in Low-Carbon Travel Behavior Intention Based on a PLS-SEM Approach," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    3. Lei Wang & Wenxiang Li & Jinxian Weng & Dong Zhang & Wanjing Ma, 2023. "Do low-carbon rewards incentivize people to ridesplitting? Evidence from structural analysis," Transportation, Springer, vol. 50(5), pages 2077-2109, October.
    4. Michel Noussan & Edoardo Campisi & Matteo Jarre, 2022. "Carbon Intensity of Passenger Transport Modes: A Review of Emission Factors, Their Variability and the Main Drivers," Sustainability, MDPI, vol. 14(17), pages 1-16, August.
    5. Hongwei Guo & Ji Han & Jian Wang, 2021. "Population mobility, urban centrality and subnetworks in China revealed by social sensing big data," Environment and Planning A, , vol. 53(8), pages 1855-1858, November.
    6. Kim, Seheon & Rasouli, Soora, 2022. "The influence of latent lifestyle on acceptance of Mobility-as-a-Service (MaaS): A hierarchical latent variable and latent class approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 304-319.
    7. Paulsen, Mads & Rich, Jeppe, 2023. "Societally optimal expansion of bicycle networks," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    8. Li, Ze-Tao & Nie, Wei-Peng & Cai, Shi-Min & Zhao, Zhi-Dan & Zhou, Tao, 2023. "Exploring the topological characteristics of urban trip networks based on taxi trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    9. Toger, Marina & Türk, Umut & Östh, John & Kourtit, Karima & Nijkamp, Peter, 2023. "Inequality in leisure mobility: An analysis of activity space segregation spectra in the Stockholm conurbation," Journal of Transport Geography, Elsevier, vol. 111(C).
    10. Brinkel, N.B.G. & Schram, W.L. & AlSkaif, T.A. & Lampropoulos, I. & van Sark, W.G.J.H.M., 2020. "Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits," Applied Energy, Elsevier, vol. 276(C).
    11. Heydarzadeh, Zahra & Mac Kinnon, Michael & Thai, Clinton & Reed, Jeff & Brouwer, Jack, 2020. "Marginal methane emission estimation from the natural gas system," Applied Energy, Elsevier, vol. 277(C).
    12. Hasselwander, Marc & Bigotte, Joao F. & Antunes, Antonio P. & Sigua, Ricardo G., 2022. "Towards sustainable transport in developing countries: Preliminary findings on the demand for mobility-as-a-service (MaaS) in Metro Manila," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 501-518.
    13. Yao Qian & Lang Sun & Quanyi Qiu & Lina Tang & Xiaoqi Shang & Chengxiu Lu, 2020. "Analysis of CO 2 Drivers and Emissions Forecast in a Typical Industry-Oriented County: Changxing County, China," Energies, MDPI, vol. 13(5), pages 1-21, March.
    14. Shi, Shuyang & Wang, Lin & Wang, Xiaofan, 2022. "Uncovering the spatiotemporal motif patterns in urban mobility networks by non-negative tensor decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    15. Yu, Qing & Li, Weifeng & Zhang, Haoran & Chen, Jinyu, 2022. "GPS data in taxi-sharing system: Analysis of potential demand and assessment of fuel consumption based on routing probability model," Applied Energy, Elsevier, vol. 314(C).
    16. Xi, Haoning & Liu, Wei & Waller, S. Travis & Hensher, David A. & Kilby, Philip & Rey, David, 2023. "Incentive-compatible mechanisms for online resource allocation in Mobility-as-a-Service systems," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 119-147.
    17. Maxwell Woody & Michael T. Craig & Parth T. Vaishnav & Geoffrey M. Lewis & Gregory A. Keoleian, 2022. "Optimizing future cost and emissions of electric delivery vehicles," Journal of Industrial Ecology, Yale University, vol. 26(3), pages 1108-1122, June.
    18. Kaiyuan Zheng & Ying Zhang, 2023. "Prediction and Urban Adaptivity Evaluation Model Based on Carbon Emissions: A Case Study of Six Coastal City Clusters in China," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    19. Liying Wang & Junya Wang & Pengxia Shen & Shangqing Liu & Shuwei Zhang, 2023. "Low-Carbon Travel Behavior in Daily Residence and Tourism Destination: Based on TPB-ABC Integrated Model," Sustainability, MDPI, vol. 15(19), pages 1-18, September.
    20. Wang, Miao & Feng, Chao, 2018. "Decomposing the change in energy consumption in China's nonferrous metal industry: An empirical analysis based on the LMDI method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2652-2663.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:331:y:2023:i:c:s0306261922016646. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.