Work

How regulatory orders and public fear affect vehicle mobility under COVID-19: A global perspective from urban overall vehicles using multi-source data

Year: 2025

Type: article

Abstract: As one of the largest pandemics in human history, COVID-19 has dealt a heavy blow to the global economy and social life. Although the pandemic situations in most countries have been stable, lessons are highly needed to learn to get more prepared for the future. In the field of transportation, the impact of COVID-19 on public transit and shared travel has been extensively studied with a relatively consistent conclusion. However, how and why the overall vehicle travel demand (including public and private) changes have not been fully addressed. This paper investigates the impact of COVID-19 on urban overall vehicle mobility as to the magnitude, duration, and driving factors. To this end, the Prophet time series model was introduced to predict vehicle mobility without COVID-19. By comparing the predicted with the real value, the mobility loss was obtained. The causes of the loss were deeply explored by applying multiple linear regression, especially focusing on the interaction effects of the strictness of regulatory orders and public fear. A large-scale dataset with more than 4 billion raw data was built for the empirical analysis from different sources, including a three-year city-level Automatic Number Plate Recognition data and data crawled from websites relating to explanatory variables. Results show that urban vehicle mobility loss almost all occurs during the national outbreak period, which is 29.66%. The factor that represents nationwide confirmed cases has a positive lagging effect on travel mobility loss, and the lag time is around seven days. Loss of urban motorized travel demand is largely caused by the interaction of perceived risks and control policy. The marginal effects of the factors work differently, indicating strong evidence of the existence of caution fatigue and shedding light on the impact mechanism.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0325118

Authors: Li Tang, Chuanli Tang, Hao Luo, Zhengbing He

Institutions: Xihua University, Beijing University of Technology

Language: English

Topic: Transportation Planning and Optimization

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Good health and well-being, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,382