气候变化领域集成服务门户(CCPortal): Real-time data from mobile platforms to evaluate sustainable transportation infrastructure

CCPortal

DOI	10.1038/s41893-020-0533-6
	Real-time data from mobile platforms to evaluate sustainable transportation infrastructure
	Asensio O.I.; Alvarez K.; Dror A.; Wenzel E.; Hollauer C.; Ha S.
发表日期	2020
ISSN	2398-9629
起始页码	463
结束页码	471
卷号	3 期号:6
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. © 2020, The Author(s), under exclusive licence to Springer Nature Limited.
语种	英语
scopus关键词	Learning algorithms; Learning systems; Supervised learning; Charging infrastructures; Computational approach; Ev charging stations; Fundamental barriers; Negative experiences; Supervised machine learning; Sustainable transportation; Transportation sector; Population statistics
来源期刊	Nature Sustainability
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/163031
作者单位	School of Public Policy and Institute for Data Engineering and Science (IDEaS), Georgia Institute of Technology, Atlanta, GA, United States; Department of Computer Science, North Carolina State University, Raleigh, NC, United States; Department of Statistical and Data Sciences and Department of Government, Smith College, Northampton, MA, United States; Department of Computer Science, Tufts University, Medford, MA, United States; H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States; School of Civil and Environmental Engineering and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, United States
推荐引用方式 GB/T 7714	Asensio O.I.,Alvarez K.,Dror A.,et al. Real-time data from mobile platforms to evaluate sustainable transportation infrastructure[J],2020,3(6).
APA	Asensio O.I.,Alvarez K.,Dror A.,Wenzel E.,Hollauer C.,&Ha S..(2020).Real-time data from mobile platforms to evaluate sustainable transportation infrastructure.Nature Sustainability,3(6).
MLA	Asensio O.I.,et al."Real-time data from mobile platforms to evaluate sustainable transportation infrastructure".Nature Sustainability 3.6(2020).