Identifying Ridesharing Risk, Response, and Challenges in the Emergence of Novel Coronavirus Using Interactions in Uber Drivers Forum
Identifying Ridesharing Risk, Response, and Challenges in the Emergence of Novel Coronavirus Using Interactions in Uber Drivers Forum
复制标题
DOI：
10.3389/fbuil.2021.619283
复制
发表时间：
2021-02-15
影响因子：
3
通讯作者：
Sadri, Arif Mohaimin
中科院分区：
文献类型：
--
作者：
Mojumder, Md Nizamul Hoque;Ahmed, Md Ashraf;Sadri, Arif Mohaimin
新型冠状病毒(新冠肺炎)大流行的爆发和出现影响了人类活动的方方面面，特别是运输部门。许多城市采取了史无前例的封锁战略，导致了严重的非必要的流动限制；因此，运输网络公司(TNC)的运营经历了重大转变。仅在美国，就有数以百万计的人在新冠肺炎爆发的早期阶段申请失业，其中许多人属于像优步/Lyft司机这样的个体户群体。由于史无前例的情况，司机和乘客都经历了巨大的挑战，可能会延长恢复过程。这项研究的目标是了解新冠肺炎大流行期间与拼车相关的风险、应对和挑战(跨国公司、司机和乘客)。因此，自新冠肺炎出现以来(2020年1月25日至5月10日)，从在线拼车论坛(即优步司机)收集了大规模的众包数据。单词二元语法、词频热图和主题模型属于不同的自然语言处理和文本挖掘技术，这些技术用于对数据进行预处理，并对重大疾病爆发期间与拼车相关的风险感知、风险承担或风险规避行为进行分类。结果表明，人们对经济混乱、刺激措施的可用性、新的就业机会、住院、流行病、个人卫生和呆在家里的担忧程度更高。此外，失业带来的前所未有的挑战，以及因分享而采取必要的个人防护行动以防止疾病传播的风险和不确定性，都是主要的相互作用。拟议对此次大流行期间共乘风险沟通动态进行的基于文本的数据分析，将有助于识别无意中影响跨国公司以及使用者(司机和乘客)的未被观察到的因素，并为即将到来的当前和未来大流行的“新常态”确定更有效的战略和替代办法。这项研究还将指导我们了解在重大危机期间如何更有效地设计和实施在线社交网站，以及如何利用此类平台在紧急情况下提供指导，以最大限度地减少因共享出行而导致的疾病传播。
The outbreak and emergence of the novel coronavirus (COVID-19) pandemic affected every aspect of human activity, especially the transportation sector. Many cities adopted unprecedented lockdown strategies that resulted in significant nonessential mobility restrictions; hence, transportation network companies (TNCs) have experienced major shifts in their operation. Millions of people alone in the USA have filed for unemployment in the early stage of the COVID-19 outbreak, many belonging to self-employed groups such as Uber/Lyft drivers. Due to unprecedented scenarios, both drivers and passengers experienced overwhelming challenges that might elongate the recovery process. The goal of this study is to understand the risk, response, and challenges associated with ridesharing (TNCs, drivers, and passengers) during the COVID-19 pandemic situation. As such, large-scale crowdsourced data were collected from online ridesharing forums (i.e., Uber Drivers) since the emergence of COVID-19 (January 25-May 10, 2020). Word bigrams, word frequency heatmaps, and topic models are among the different natural language processing and text-mining techniques used to preprocess the data and classify risk perception, risk-taking, or risk-averting behaviors associated with ridesharing during a major disease outbreak. Results indicate higher levels of concern about economic disruption, availability of stimulus checks, new employment opportunities, hospitalization, pandemic, personal hygiene, and staying at home. In addition, unprecedented challenges due to unemployment and the risk and uncertainties in the required personal protective actions against spreading the disease due to sharing are among the major interactions. The proposed text-based data analytics of the ridesharing risk communication dynamics during this pandemic will help to identify unobserved factors inadvertently affecting the TNCs as well as the users (drivers and passengers) and identify more efficient strategies and alternatives for the forthcoming "new normal" of the current pandemic and the ones in the future. The study will also guide us toward understanding how efficiently online social interaction outlets can be designed and implemented more effectively during a major crisis and how to leverage such platforms for providing guidelines during emergencies to minimize transmission of disease due to shared travel.