Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston

Estimating long-term and short-term impact of COVID-19 activity restriction on regional highway traffic demand: A case study in Zhejiang Province, China

Since the outbreak of COVID-19 in China in late 2019, government administrators have implemented traffic restriction policies to prevent the spread of COVID-19. However, highway traffic volumes obtained from ETC data in some provinces did not return to the levels of previous years after the end of the traffic restriction policy, suggesting that traffic restriction policy may have long-term effects. This paper proposed a method that analyzes traffic restriction policies' long-term and short-term impact on highway traffic volume under COVID-19. This method first analyzes the long-term and short-term impacts of traffic restriction policies on the highway traffic volume using the Prophet model combined with the concept of traffic volume loss. It further investigates the relationship between COVID-19 cases and the long-term and short-term impacts of the traffic restriction policy using Granger causality and the impulse response function of the Bayesian vector autoregressive (BVAR) model. The results showed that during the COVID-19 pandemic, highway traffic in Zhejiang Province decreased by about 95.5%, and the short-term impact of COVID-19 cases was most pronounced on the second day. However, the long-term effects were relatively small when the traffic restriction policy ended and was verified by data from other provinces. These results will provide decision support for traffic management and provide recommendations for future traffic impact assessments in the event of similar epidemics.

Understanding the motivational mechanisms behind the usage frequency of ride-hailing during COVID-19 pandemic

Introduction

This study aimed to explore the factors influencing people's utilization of ride-hailing services, particularly in the context of the COVID-19 pandemic.

Methods

A two-stage survey was conducted among the same group of passengers pre and post COVID-19 pandemic, resulting in a total of 670 valid samples. Exploratory factor analysis (EFA) was applied to the data, followed by the ordered probit and ordered logit models to identify the motivational factors behind passengers' frequency of using ride-hailing.

Results

The findings indicated that trust and loyalty were the most influential factors in determining passengers' frequency of using ride-hailing services. However, passengers' perception of the COVID-19 pandemic did not have a significant effect on the frequency of using ride-hailing.

Discussion

This research provides empirical evidence and policy implications for understanding people's usage of the ride-hailing services in the context of public-health emergency.