energies Article Spatiotemporal Patterns of Carbon Emissions and Taxi Travel Using GPS Data in Beijing Jinlei Zhang 1, Feng Chen 1,2, Zijia Wang 1,* ID , Rui Wang 1 and Shunwei Shi 1 1 School of Civil and Architectural Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China;
[email protected] (J.Z.);
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[email protected] (S.S.) 2 Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention, No.3 Shangyuancun, Haidian District, Beijing 100044, China * Correspondence:
[email protected]; Tel.: +86-010-5168-8070 Received: 21 December 2017; Accepted: 19 February 2018; Published: 27 February 2018 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).