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Visual Analytics of CO\(_2\) Emissions from Individuals’ Daily Travel Based on Large-Scale Taxi Trajectories

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Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14497))

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Abstract

Understanding the patterns of traffic-related carbon dioxide (CO\(_2\)) emissions from different trip purposes is of great significance for the development of low-carbon transportation. However, most existing research ignores the traffic-related CO\(_2\) emissions from daily trip. Accurately inferring trip purposes is a prerequisite for analyzing the patterns of traffic-related CO\(_2\) emissions from daily trip. The existing research on inferring trip purposes has been proven effective, but it ignores door-to-door service (DTD) and the time-varying characteristics of the attractiveness of Points of Interest (POIs). In this paper, we propose a Bayesian-based method to infer trip purposes. It identifies DTD through spatial relation operations and constructs the dynamic function of POIs attractiveness using kernel density estimation (KDE). A visual analysis system is also developed to help users explore the spatio-temporal patterns of traffic-related CO\(_2\) emissions from daily trip. Finally, the effectiveness of the method and the system is verified through case study based on real data and positive feedback from experts.

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Acknowledgements

This work was supported by Natural Science Foundation of Sichuan Province (Grant No. 2022NSFSC0961) the Ph.D. Research Foundation of Southwest University of Science and Technology (Grant No. 19zx7144) the Special Research Foundation of China (Mianyang) Science and Technology City Network Emergency Management Research Center (Grant No. WLYJGL2023ZD04).

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Authors and Affiliations

  1. School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China

    Dongliang Ma, Song Wang, Liang Liu & Hao Hu

  2. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

    Liang Liu

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  1. Dongliang Ma
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  2. Song Wang
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  3. Liang Liu
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  4. Hao Hu
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Corresponding author

Correspondence to Song Wang .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Shanghai Jiao Tong University, Shanghai, China

    Lei Bi

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. MIRALab-CUI, University of Geneva, Carouge, Geneve, Switzerland

    Nadia Magnenat-Thalmann

  5. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

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Ma, D., Wang, S., Liu, L., Hu, H. (2024). Visual Analytics of CO\(_2\) Emissions from Individuals’ Daily Travel Based on Large-Scale Taxi Trajectories. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14497. Springer, Cham. https://doi.org/10.1007/978-3-031-50075-6_17

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  • DOI: https://doi.org/10.1007/978-3-031-50075-6_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50074-9

  • Online ISBN: 978-3-031-50075-6

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