IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v124y2025ics0966692325000493.html
   My bibliography  Save this article

Modeling and spatial analysis of heavy-duty truck CO2 using travel activities

Author

Listed:
  • Peng, Zhipeng
  • Ji, Hao
  • Yuan, RenTeng
  • Wang, Yonggang
  • Easa, Said M.
  • Wang, Chenzhu
  • Cui, Hongshuai
  • Zhao, Xiatong

Abstract

Heavy-duty trucks (HDTs) are vital components of the freight industry yet have faced criticism for their substantial CO2 emissions. This study, focusing on Xi'an, a crucial freight hub city in China, aims to investigate the factors influencing CO2 emission from HDTs. A unique aspect of this study is using a Latent Dirichlet Allocation (LDA) model to evaluate the potential impact of different travel activities on CO2 emissions using travel activities of HDTs extracted from extensive GPS data. Subsequently, the Random Forest (RF) model with a GeoShapley explainer was used to examine both the main and spatial effects of travel activities, road density, land use, and freight hub accessibility on CO2 emissions. The results revealed the existence of fifteen distinct travel activities among HDTs in Xi'an, eight of which clearly influence CO2 emissions. Considerable variations were observed in the magnitudes of the impact of different variables on CO2 emissions, as indicated by GeoShapley values. The density of expressways and main roads has the greatest impact on CO2 emissions, while various types of travel activities also significantly affect CO2 emissions, with the impact of different travel activities varying to some extent. Additionally, there is evident spatial heterogeneity in the impact of various variables on CO2 emissions, with larger positive GeoShapley values tending to concentrate around the 3rd Ring and expressways in Xi'an City. These findings, shedding light on the complex interplay of factors influencing CO2 emissions from HDTs, provide valuable insights for formulating environmentally sustainable management policies concerning HDTs from spatial perspectives.

Suggested Citation

  • Peng, Zhipeng & Ji, Hao & Yuan, RenTeng & Wang, Yonggang & Easa, Said M. & Wang, Chenzhu & Cui, Hongshuai & Zhao, Xiatong, 2025. "Modeling and spatial analysis of heavy-duty truck CO2 using travel activities," Journal of Transport Geography, Elsevier, vol. 124(C).
    • Handle: RePEc:eee:jotrge:v:124:y:2025:i:c:s0966692325000493
    DOI: 10.1016/j.jtrangeo.2025.104158
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0966692325000493
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jtrangeo.2025.104158?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
    ---><---

    References listed on IDEAS

    as
    1. Ji, Shujuan & Wang, Xin & Lyu, Tao & Liu, Xiaojie & Wang, Yuanqing & Heinen, Eva & Sun, Zhenwei, 2022. "Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis," Journal of Transport Geography, Elsevier, vol. 103(C).
    2. Shiraki, Hiroto & Matsumoto, Ken'ichi & Shigetomi, Yosuke & Ehara, Tomoki & Ochi, Yuki & Ogawa, Yuki, 2020. "Factors affecting CO2 emissions from private automobiles in Japan: The impact of vehicle occupancy," Applied Energy, Elsevier, vol. 259(C).
    3. Bi, Hui & Li, Aoyong & Hua, Mingzhuang & Zhu, He & Ye, Zhirui, 2022. "Examining the varying influences of built environment on bike-sharing commuting: Empirical evidence from Shanghai," Transport Policy, Elsevier, vol. 129(C), pages 51-65.
    4. Dalla Chiara, Giacomo & Goodchild, Anne, 2020. "Do commercial vehicles cruise for parking? Empirical evidence from Seattle," Transport Policy, Elsevier, vol. 97(C), pages 26-36.
    5. Chen, Chao & Feng, Tao & Ding, Chuan & Yu, Bin & Yao, Baozhen, 2021. "Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model," Journal of Transport Geography, Elsevier, vol. 96(C).
    6. Ziqi Li, 2024. "GeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Models," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 114(7), pages 1365-1385, August.
    7. Xie, Qichang & Liu, Junxian, 2019. "Combined nonlinear effects of economic growth and urbanization on CO2 emissions in China: Evidence from a panel data partially linear additive model," Energy, Elsevier, vol. 186(C).
    8. 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).
    9. Wang, Qian & Zhu, Hongtao, 2024. "Combined top-down and bottom-up approach for CO2 emissions estimation in building sector of beijing: Taking new energy vehicles into consideration," Energy, Elsevier, vol. 290(C).
    10. Bigazzi, Alexander, 2019. "Comparison of marginal and average emission factors for passenger transportation modes," Applied Energy, Elsevier, vol. 242(C), pages 1460-1466.
    11. Nicolás Deschle & Ernst Jan van Ark & René van Gijlswijk & Robbert Janssen, 2022. "Impact of Signalized Intersections on CO 2 and NO x Emissions of Heavy Duty Vehicles," Energies, MDPI, vol. 15(3), pages 1-19, February.
    12. González, Rosa Marina & Marrero, Gustavo A. & Rodríguez-López, Jesús & Marrero, Ángel S., 2019. "Analyzing CO2 emissions from passenger cars in Europe: A dynamic panel data approach," Energy Policy, Elsevier, vol. 129(C), pages 1271-1281.
    13. 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).
    14. Kemajou, Armel & Jaligot, Rémi & Bosch, Martí & Chenal, Jérôme, 2019. "Assessing motorcycle taxi activity in Cameroon using GPS devices," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    15. Yang, Zhiwei & Chen, Xiaohong & Deng, Jihao & Li, Tianhao & Yuan, Quan, 2023. "Footprints of goods movements: Spatial heterogeneity of heavy-duty truck activities and its influencing factors in the urban context," Journal of Transport Geography, Elsevier, vol. 113(C).
    16. Tiangui Lv & Han Hu & Hualin Xie & Xinmin Zhang & Li Wang & Xiaoqiang Shen, 2023. "An empirical relationship between urbanization and carbon emissions in an ecological civilization demonstration area of China based on the STIRPAT model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(3), pages 2465-2486, March.
    17. Lyu, Tao & Wang, Yuanqing & Ji, Shujuan & Feng, Tao & Wu, Zhouhao, 2023. "A multiscale spatial analysis of taxi ridership," Journal of Transport Geography, Elsevier, vol. 113(C).
    18. Guanwei Zhao & Zeyu Pan & Muzhuang Yang, 2022. "Marginal Effects and Spatial Variations of the Impact of the Built Environment on Taxis’ Pollutant Emissions in Chengdu, China," IJERPH, MDPI, vol. 19(24), pages 1-19, December.
    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. Ziqi Li, 2025. "Explainable AI in Spatial Analysis," Papers 2505.00591, arXiv.org.

    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. Yang, Zhiwei & Liu, Han & Chen, Xiaohong & Zhou, Jun & Yuan, Quan, 2025. "Discovering the origins of freight demand: An empirical investigation of spatial heterogeneity in the generation of heavy-duty truck trips," Transport Policy, Elsevier, vol. 164(C), pages 60-79.
    2. 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).
    3. Yang, Zhiwei & Chen, Xiaohong & Deng, Jihao & Li, Tianhao & Yuan, Quan, 2023. "Footprints of goods movements: Spatial heterogeneity of heavy-duty truck activities and its influencing factors in the urban context," Journal of Transport Geography, Elsevier, vol. 113(C).
    4. Lu, Wenbo & Zhang, Yong & Xu, Jinhua & Yuan, Zheng & Li, Peikun & Zhang, Mingye & Vu, Hai L., 2025. "Exploring spatiotemporal heterogeneity of urban green freight delivery parking based on new energy vehicle GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 193(C).
    5. Wu, Jishi & Feng, Tao & Jia, Peng & Li, Gen, 2024. "Spatial allocation of heavy commercial vehicles parking areas through geo-fencing," Journal of Transport Geography, Elsevier, vol. 117(C).
    6. Xiao, Lin & Guan, Yuru & Guo, Yaqin & Xue, Rui & Li, Jiashuo & Shan, Yuli, 2022. "Emission accounting and drivers in 2004 EU accession countries," Applied Energy, Elsevier, vol. 314(C).
    7. Li, Zhaoxiang & Ma, Xu & Pan, Ruixu & Yang, Chao & Yuan, Quan, 2025. "Explaining the dynamics and identifying potential risks of hazardous materials truck movements," Journal of Transport Geography, Elsevier, vol. 123(C).
    8. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    9. Chahine, Ricardo & Duarte, Jorge & Gkritza, Konstantina, 2025. "Effect of protected bike lanes on bike-sharing ridership: A New York City case study," Journal of Transport Geography, Elsevier, vol. 123(C).
    10. 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.
    11. Ouyang, Kechen & Wang, David Z.W., 2025. "Optimal operation strategies for freight transport with electric vehicles considering wireless charging lanes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 193(C).
    12. Wan, Tianyi & Fu, Hao & Li, Xiaoshan & Wu, Fan & Luo, Cong & Zhang, Liqi, 2025. "Assessment of decarbonization pathway for Chinese road transport sector based on transportation-energy integration systems framework," Energy, Elsevier, vol. 317(C).
    13. Isik, Mine & Ari, Izzet & Sarica, Kemal, 2021. "Challenges in the CO2 emissions of the Turkish power sector: Evidence from a two-level decomposition approach," Utilities Policy, Elsevier, vol. 70(C).
    14. Alberto Ponso & Angelo Bonfitto & Giovanni Belingardi, 2023. "Route Planning for Electric Vehicles Including Driving Style, HVAC, Payload and Battery Health," Energies, MDPI, vol. 16(12), pages 1-22, June.
    15. Abhishek, & Legros, Benjamin & Fransoo, Jan C., 2021. "Performance evaluation of stochastic systems with dedicated delivery bays and general on-street parking," Other publications TiSEM 09ed9572-d59c-4f28-a9c4-b, Tilburg University, School of Economics and Management.
    16. Haseeb, Attiya & Mitra, Raktim, 2024. "Travel behaviour changes among young adults and associated implications for social sustainability," Transportation Research Part A: Policy and Practice, Elsevier, vol. 187(C).
    17. 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).
    18. 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).
    19. Narayanan, Santhanakrishnan & Antoniou, Constantinos, 2022. "Electric cargo cycles - A comprehensive review," Transport Policy, Elsevier, vol. 116(C), pages 278-303.
    20. Shabir Mohsin Hashmi & Xuyou Yu & Qasim Raza Syed & Li Rong, 2024. "Testing the environmental Kuznets curve (EKC) hypothesis amidst climate policy uncertainty: sectoral analysis using the novel Fourier ARDL approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 16503-16522, July.

    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:jotrge:v:124:y:2025:i:c:s0966692325000493. 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: https://www.journals.elsevier.com/journal-of-transport-geography .

    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.