Abstract
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.
Highlights
AND MOTIVATIONThe emergence and outbreak of the novel coronavirus (COVID19) affected every aspect of our daily lives
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
The COVID-19 turned into a pandemic; many cities adopted unprecedented lockdown strategies that resulted in significant nonessential mobility restrictions
Summary
AND MOTIVATIONThe emergence and outbreak of the novel coronavirus (COVID19) affected every aspect of our daily lives. China, was among the first of the cities that went through major lockdown activities (December 2019); the disease continued to spread. The COVID-19 turned into a pandemic; many cities adopted unprecedented lockdown strategies that resulted in significant nonessential mobility restrictions. Working from home and online shopping have become more prominent, providing people with new opportunities in terms of income and sharing economy. Li et al addressed the productivity of shared mobility providers in emergency situations and highlighted the increase of TNCs such as Uber and DiDi (Li et al, 2018). Wong et al suggested that the sharing economy is relevant to transport and resource shelter in crises, as public authorities still lack adequate resources during disaster and shelter for all residents in emergency situations (Wong and Shaheen, 2019)
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