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Spatiotemporal Patterns of Carbon Emissions and Taxi Travel Using GPS Data in Beijing

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  • Jinlei Zhang

    (School of Civil and Architectural Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China)

  • Feng Chen

    (School of Civil and Architectural Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China
    Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention, No.3 Shangyuancun, Haidian District, Beijing 100044, China)

  • Zijia Wang

    (School of Civil and Architectural Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China)

  • Rui Wang

    (School of Civil and Architectural Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China)

  • Shunwei Shi

    (School of Civil and Architectural Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China)

Abstract

Taxis are significant contributors to carbon dioxide emissions due to their frequent usage, yet current research into taxi carbon emissions is insufficient. Emerging data sources and big data–mining techniques enable analysis of carbon emissions, which contributes to their reduction and the promotion of low-carbon societies. This study uses taxi GPS data to reconstruct taxi trajectories in Beijing. We then use the carbon emission calculation model based on a taxi fuel consumption algorithm and the carbon dioxide emission factor to calculate emissions and apply a visualization method called kernel density analysis to obtain the dynamic spatiotemporal distribution of carbon emissions. Total carbon emissions show substantial temporal variations during the day, with maximum values from 10:00–11:00 (57.53 t), which is seven times the minimum value of 7.43 t (from 03:00–04:00). Carbon emissions per kilometer at the network level are steady throughout the day (0.2 kg/km). The Airport Expressway, Ring Roads, and large intersections within the 5th Ring Road maintain higher carbon emissions than other areas. Spatiotemporal carbon emissions and travel patterns differ between weekdays and weekends, especially during morning rush hours. This research provides critical insights for taxi companies, authorities, and future studies.

Suggested Citation

  • Jinlei Zhang & Feng Chen & Zijia Wang & Rui Wang & Shunwei Shi, 2018. "Spatiotemporal Patterns of Carbon Emissions and Taxi Travel Using GPS Data in Beijing," Energies, MDPI, vol. 11(3), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:500-:d:133556
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    References listed on IDEAS

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    1. Yang, Lin & Kwan, Mei-Po & Pan, Xiaofang & Wan, Bo & Zhou, Shunping, 2017. "Scalable space-time trajectory cube for path-finding: A study using big taxi trajectory data," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 1-27.
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    Cited by:

    1. Lixin Yan & Bowen Sheng & Yi He & Shan Lu & Junhua Guo, 2022. "Forecasting and Planning Method for Taxi Travel Combining Carbon Emission and Revenue Factors—A Case Study in China," IJERPH, MDPI, vol. 19(18), pages 1-20, September.
    2. Song Li & Fei Xue & Chuyu Xia & Jian Zhang & Ao Bian & Yuexi Lang & Jun Zhou, 2022. "A Big Data-Based Commuting Carbon Emissions Accounting Method—A Case of Hangzhou," Land, MDPI, vol. 11(6), pages 1-18, June.
    3. 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.
    4. Aleksander Król & Małgorzata Król, 2019. "A Stochastic Simulation Model for the Optimization of the Taxi Management System," Sustainability, MDPI, vol. 11(14), pages 1-22, July.

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