期刊
APPLIED ENERGY
卷 250, 期 -, 页码 147-160出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2019.04.157
关键词
Ride-hailing; Urban mobility; GPS trajectories; Electrification; Machine learning; Big data
资金
- National Natural Science Foundation of China [41401444]
- Basic Research Program of Shenzhen Science and Technology Innovation Committee [JCYJ20180305125113883, JCYJ20170302144402023]
- China Scholarship Council [201708440434]
- Allianz
- Amsterdam Institute for Advanced Metropolitan Solutions
- Brose
- Cisco
- Ericsson
- Fraunhofer Institute
- Liberty Mutual Institute
- Kuwait-MIT Center for Natural Resources and the Environment
- Shenzhen
- Singapore-MIT Alliance for Research and Technology (SMART)
- UBER
- Vitoria State Government
- Volkswagen Group America
The acceptability, energy consumption, and environmental benefits of electric vehicles are highly dependent on travel patterns. With increasing ride-hailing popularity in mega-cities, urban mobility patterns are greatly changing; therefore, an investigation of the extent to which electric vehicles would satisfy the needs of ride-hailing drivers becomes important to support sustainable urban growth. A first step in this direction is reported here. GPS-trajectories of 144,867 drivers over 104 million km in Beijing were used to quantify the potential acceptability, energy consumption, and costs of ride-hailing electric vehicle fleets. Average daily travel distance and travel time for ride-hailing drivers was determined to be 129.4 km and 5.7 h; these values are substantially larger than those for household drivers (40.0 km and 1.5 h). Assuming slow level-1 (1.8 KW) or moderate level-2 (7.2 KW) charging is available at all home parking locations, battery electric vehicles with 200 km all electric range (BEV200) could be used by up to 47% or 78% of ride-hailing drivers and electrify up to 20% or 55% of total distance driven by the ride-hailing fleet. With level-2 charging available at home, work, and public parking, the acceptance ceiling increases to up to 91% of drivers and 80% of distance. Our study suggests that long range BEVs and widespread level-2 charging infrastructure are needed for large-scale electrification of ride-hailing mobility in Beijing. The marginal benefits of increased all electric range, effects on charging infrastructure distribution, and payback times are also presented and discussed. Given the observed heterogeneity of ride hailing vehicle travel, our study outlines the importance of individual-level analysis to understand the electrification potential and future benefits of electric vehicles in the era of shared smart transportation.
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