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Energies 2018, 11(3), 500; https://doi.org/10.3390/en11030500

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

1
School of Civil and Architectural Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China
2
Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention, No.3 Shangyuancun, Haidian District, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Received: 21 December 2017 / Revised: 11 February 2018 / Accepted: 19 February 2018 / Published: 27 February 2018
(This article belongs to the Special Issue The Governance of Sustainable Cities and Innovative Transport)
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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. View Full-Text
Keywords: taxi GPS data; carbon emission; dynamic spatiotemporal distribution; kernel density analysis taxi GPS data; carbon emission; dynamic spatiotemporal distribution; kernel density analysis
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Zhang, J.; Chen, F.; Wang, Z.; Wang, R.; Shi, S. Spatiotemporal Patterns of Carbon Emissions and Taxi Travel Using GPS Data in Beijing. Energies 2018, 11, 500.

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