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Modeling Traffic Flow, Energy Use, and Emissions Using Google Maps and Google Street View: The Case of EDSA, Philippines

Author

Listed:
  • Joshua Ezekiel Rito

    (Mechanical Engineering Department, De La Salle University, Manila 0922, Philippines)

  • Neil Stephen Lopez

    (Mechanical Engineering Department, De La Salle University, Manila 0922, Philippines)

  • Jose Bienvenido Manuel Biona

    (Mechanical Engineering Department, De La Salle University, Manila 0922, Philippines
    Enrique Razon Logistics Institute, De La Salle University, Manila 0922, Philippines)

Abstract

The general framework of the bottom-up approach for modeling mobile emissions and energy use involves the following major components: (1) quantifying traffic flow and (2) calculating emission and energy consumption factors. In most cases, researchers deal with complex and arduous tasks, especially when conducting actual surveys in order to calculate traffic flow. In this regard, the authors are introducing a novel method in estimating mobile emissions and energy use from road traffic flow utilizing crowdsourced data from Google Maps. The method was applied on a major highway in the Philippines commonly known as EDSA. Results showed that a total of 370,855 vehicles traveled along EDSA on average per day in June 2019. In comparison to a government survey, only an 8.63% error was found with respect to the total vehicle count. However, the approximation error can be further reduced to 4.63% if cars and utility vehicles are combined into one vehicle category. The study concludes by providing the limitations and opportunities for future work of the proposed methodology.

Suggested Citation

  • Joshua Ezekiel Rito & Neil Stephen Lopez & Jose Bienvenido Manuel Biona, 2021. "Modeling Traffic Flow, Energy Use, and Emissions Using Google Maps and Google Street View: The Case of EDSA, Philippines," Sustainability, MDPI, vol. 13(12), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:12:p:6682-:d:573756
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    References listed on IDEAS

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    Cited by:

    1. S. Roderick Zhang & Bilal Farooq, 2022. "Interpretable and Actionable Vehicular Greenhouse Gas Emission Prediction at Road link-level," Papers 2206.09073, arXiv.org.
    2. Agnieszka Dudziak & Jacek Caban & Ondrej Stopka & Monika Stoma & Marie Sejkorová & Mária Stopková, 2023. "Vehicle Market Analysis of Drivers’ Preferences in Terms of the Propulsion Systems: The Czech Case Study," Energies, MDPI, vol. 16(5), pages 1-20, March.

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