A regression-content analysis approach to assess public satisfaction with shared mobility measures against COVID-19 pandemic

Abstract

Introduction

The transportation sector was severely impacted by the COVID-19 pandemic, with shared mobility services being the most affected due to concerns from the public regarding the high likelihood of being a vector of the virus. Although studies have evaluated the impact of the COVID-19 pandemic on shared mobility, a deeper understanding of public satisfaction with the measures adopted during COVID-19 has not been explored.

Methods

This study utilized data collected in the Summer of 2020 across the United States to fill that literature gap. The study applied Ordered Probit (OP) models to explore the factors influencing an individual's confidence in not contracting COVID-19 while using shared mobility modes and Text Network Analysis (TNA) to understand the deeper reasons for their confidence levels.

Results

Results show a significant influence of sociodemographic factors, land-use/built environment, pre- and post-COVID travel behavior, and activity participation on respondents’ level of confidence for not contracting COVID-19. Only frequent public transit users showed that they have high confidence in not getting COVID-19 when they use any of the shared mobility options, while people who did not use public transit and those who frequently attend telehealth meetings had low confidence in the measures adopted by shared mobility providers. Furthermore, the text mining results indicated that cleanness was the key theme regardless of the confidence level of the respondents, except for rail and bus transit. However, we observed other patterns of themes across the types of shared mobility.

Conclusions

The study findings can be beneficial in the future to improve ridership during pandemics by considering perceptions and satisfactions of various users.