Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1038/s41893-020-0533-6 |
Real-time data from mobile platforms to evaluate sustainable transportation infrastructure | |
Asensio, Omar Isaac1,2; Alvarez, Kevin3; Dror, Arielle4,5; Wenzel, Emerson6; Hollauer, Catharina7; Ha, Sooji8,9 | |
2020-06-01 | |
发表期刊 | NATURE SUSTAINABILITY |
ISSN | 2398-9629 |
出版年 | 2020 |
卷号 | 3期号:6页码:463-+ |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | By displacing gasoline and diesel fuels, electric cars and fleets reduce emissions from the transportation sector, thus offering important public health benefits. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to adoption. Using large-scale social data and machine-learning based on 12,720 electric vehicle (EV) charging stations, we provide national evidence on how well the existing charging infrastructure is serving the needs of the rapidly expanding population of EV drivers in 651 core-based statistical areas in the United States. We deploy supervised machine-learning algorithms to automatically classify unstructured text reviews generated by EV users. Extracting behavioural insights at a population scale has been challenging given that streaming data can be costly to hand classify. Using computational approaches, we reduce processing times for research evaluation from weeks of human processing to just minutes of computation. Contrary to theoretical predictions, we find that stations at private charging locations do not outperform public charging locations provided by the government. Overall, nearly half of drivers who use mobility applications have faced negative experiences at EV charging stations in the early growth years of public charging infrastructure, a problem that needs to be fixed as the market for electrified and sustainable transportation expands. A reliable charging infrastructure is critical to wider adoption of electric cars. With large-scale social data and machine intelligence, this study shows the importance of the quality, not just the quantity, of charging stations to consumers, suggesting policy design should include consumer data. |
领域 | 资源环境 |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000537034200002 |
WOS关键词 | FRACTIONAL RESPONSE VARIABLES ; ELECTRIC VEHICLE ADOPTION ; INCENTIVES ; MARKETS ; SERVICE |
WOS类目 | Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies |
WOS研究方向 | Science & Technology - Other Topics ; Environmental Sciences & Ecology |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/273353 |
专题 | 资源环境科学 |
作者单位 | 1.Georgia Inst Technol, Sch Publ Policy, Atlanta, GA 30332 USA; 2.Georgia Inst Technol, Inst Data Engn & Sci IDEaS, Atlanta, GA 30332 USA; 3.North Carolina State Univ, Dept Comp Sci, Raleigh, NC USA; 4.Smith Coll, Dept Stat & Data Sci, Northampton, MA 01063 USA; 5.Smith Coll, Dept Govt, Northampton, MA 01063 USA; 6.Tufts Univ, Dept Comp Sci, Medford, MA 02155 USA; 7.Georgia Inst Technol, H Milton Stewart Sch Ind & Syst Engn, Atlanta, GA 30332 USA; 8.Georgia Inst Technol, Sch Civil & Environm Engn, Atlanta, GA 30332 USA; 9.Georgia Inst Technol, Sch Computat Sci & Engn, Atlanta, GA 30332 USA |
推荐引用方式 GB/T 7714 | Asensio, Omar Isaac,Alvarez, Kevin,Dror, Arielle,et al. Real-time data from mobile platforms to evaluate sustainable transportation infrastructure[J]. NATURE SUSTAINABILITY,2020,3(6):463-+. |
APA | Asensio, Omar Isaac,Alvarez, Kevin,Dror, Arielle,Wenzel, Emerson,Hollauer, Catharina,&Ha, Sooji.(2020).Real-time data from mobile platforms to evaluate sustainable transportation infrastructure.NATURE SUSTAINABILITY,3(6),463-+. |
MLA | Asensio, Omar Isaac,et al."Real-time data from mobile platforms to evaluate sustainable transportation infrastructure".NATURE SUSTAINABILITY 3.6(2020):463-+. |
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