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Real-time data from mobile platforms to evaluate sustainable transportation infrastructure

机译：从移动平台评估实时数据可持续发展的交通基础设施

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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.
机译：取代汽油和柴油燃料,电力汽车和舰队降低排放量交通行业,从而提供重要的公共卫生的好处。充电的可靠性的信心基础设施仍然是一个根本性的障碍采用。机器学习基于12720电动汽车电动汽车充电站,我们提供国家证据如何现有的收费基础设施服务的需要迅速扩张的人口651年电动汽车司机为核心的统计在美国地区州。算法自动分类结构化生成的文本评论电动汽车用户。在一个人口规模行为的见解是具有挑战性的,因为流数据成本分类。方法,我们减少处理时间研究评估周的人类处理的几分钟计算。与理论预测,我们发现站在私人地方不收费比公共充电所提供的位置政府。使用移动应用程序面临负谁在早期的电动汽车充电站的经历增长多年的公共充电基础设施,问题,需要固定的市场电气化和可持续的交通扩张。

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