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Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel

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  • Zhao, Pengxiang
  • Kwan, Mei-Po
  • Qin, Kun

Abstract

Traffic-related carbon dioxide (CO2) emissions have become a major problem in cities. Especially, the CO2 emissions induced by taxis account for a high proportion in total CO2 emissions. The availability of taxi trajectory data presents new opportunities for addressing CO2 emissions induced by taxis. Few previous studies have analyzed the impact of human trips on CO2 emissions. This paper investigates trip-related CO2 emission patterns based on individuals' travel behavior using taxi trajectory data. First, we propose a trip purpose inference method that takes into account the spatiotemporal attractiveness of POIs to divide human trips into different types. Further, we reveal the spatiotemporal patterns of CO2 emissions from various types of trips, including temporal regularity and periodicity as well as spatial distribution of “black areas”. Finally, comparative analysis of CO2 emissions for different kinds of trips based on trip behavior is conducted using three variables, namely trip distance, trip duration and trip speed. This study is helpful for us to understand how to make travel and cities more sustainable through modifying people's trip behaviors or taxi trips.

Suggested Citation

  • Zhao, Pengxiang & Kwan, Mei-Po & Qin, Kun, 2017. "Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel," Journal of Transport Geography, Elsevier, vol. 62(C), pages 122-135.
    • Handle: RePEc:eee:jotrge:v:62:y:2017:i:c:p:122-135
    DOI: 10.1016/j.jtrangeo.2017.05.001
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    11. Li, Shaoying & Zhuang, Caigang & Tan, Zhangzhi & Gao, Feng & Lai, Zhipeng & Wu, Zhifeng, 2021. "Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China," Journal of Transport Geography, Elsevier, vol. 91(C).
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