Unlocking the Power of Mobile Phone Application Data to Accelerate Transport Decarbonisation (Short Paper)

Authors Xianghui Zhang , Tao Cheng



PDF
Thumbnail PDF

File

LIPIcs.GIScience.2023.92.pdf
  • Filesize: 1.7 MB
  • 6 pages

Document Identifiers

Author Details

Xianghui Zhang
  • SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College London, UK
Tao Cheng
  • SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College London, UK

Cite AsGet BibTex

Xianghui Zhang and Tao Cheng. Unlocking the Power of Mobile Phone Application Data to Accelerate Transport Decarbonisation (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 92:1-92:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.92

Abstract

Decarbonising transport is crucial in addressing climate change and achieving the Net Zero target. However, limitations arising from traditional data sources and methods obstruct the provision of individual travel information with comprehensive travel modes, high spatiotemporal granularity and updating frequency for achieving transport decarbonisation. Mobile phone application data, an essentially new form of data, can provide valuable travel information after effective mining and assist in progress monitoring, policy evaluation, and system optimisation in transport decarbonisation. This paper proposes a standardised methodology to unlock the power of mobile phone application data for supporting transport decarbonisation. Three typical cases are employed to demonstrate the capabilities of the generated individual multimodal dataset, including monitoring Londoners’ 20-minute active travel target, transport GHGs emissions and their contributors, and evaluating small-scale transport interventions. The paper also discusses the limitations of mobile phone application data, such as issues surrounding data privacy and regulation.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
  • Applied computing → Transportation
Keywords
  • Transport decarbonisation
  • Mobile phone application data
  • Application
  • London

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Adel Bolbol, Tao Cheng, Ioannis Tsapakis, and James Haworth. Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification. Computers, Environment and Urban Systems, 36(6):526-537, 2012. URL: https://doi.org/10.1016/j.compenvurbsys.2012.06.001.
  2. Department for Transport. Decarbonising Transport - A Better, Greener Britain. Technical report, United Kingdom Government, 2021. Google Scholar
  3. IEA. Transport, IEA. Technical report, International Energy Agency, 2022. Google Scholar
  4. Peilin Li, Pengjun Zhao, and Christian Brand. Future energy use and CO2 emissions of urban passenger transport in China: A travel behavior and urban form based approach. Applied Energy, 211(October 2017):820-842, 2018. URL: https://doi.org/10.1016/j.apenergy.2017.11.022.
  5. Xianghui Zhang and Tao Cheng. The impacts of the COVID-19 pandemic on multimodal human mobility in London: A perspective of decarbonizing transport. Geo-spatial Information Science, 00(00):1-13, 2022. URL: https://doi.org/10.1080/10095020.2022.2122876.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail