Impacts of COVID-19 Pandemic on Travel Behavior in Large Cities of China: Investigation on the Lockdown and Reopening Phases

Abstract

The COVID-19 pandemic has caused worldwide lockdowns and similar containment measures aiming to curb the spread of the virus. Lockdown measures have been implemented in cities amid the COVID-19 outbreak. After the pandemic is under control, cities will be gradually reopened. This study aims to investigate the variations in urban travel behavior during the lockdown and reopening phases. On the basis of long-term traffic congestion index data and subway ridership data in eight typical cities of China, this study carried out comparisons on urban travel behaviors with and without the pandemic. Changes in the multimodal travel behaviors in different times of day and days of week are analyzed during the lockdown and reopening phases. Multivariate and one-way analyses of variance are conducted to show the statistical significance of the changes. This study further investigates the relationship between the returned-to-work (RTW) rate and travel behaviors in the reopening phase. A stepwise multiple regression is conducted to quantify the impacts of influencing factors (i.e., population migration index, RTW rate, socioeconomic indices, and pandemic statistical indicators) on vehicular traffic after reopening. Results show that the lockdown measure has a significant impact on reducing the traffic congestion during the peak hours on workdays, and the subway ridership dropped to below 10% of the prepandemic level during the lockdown phase. Travel demands tended to switch from subways to private vehicular travel modes during the reopening phase, leading to a rapid recovery of vehicular traffic and a slow recovery of subway ridership. The recovery of vehicular traffic is proved to be related to the RTW rate, certain city characteristics, and new COVID-19 cases after city reopening.

Changes to the Transport Behaviour of Inhabitants of a Large City Due the Pandemic

“…However, scientists still lack sufficiently detailed data on how population mobility is evolving after the pandemic and whether (and if so, how) the criteria for choosing modes of public transport will change. While there are studies addressing this phenomenon [85][86][87], it is difficult to clearly indicate how universal their results are and to what extent they only show changes typical of the society these authors analysed. For this reason, the quantitative picture presented above requires the research to be expanded with information portraying post-pandemic mobility in Łódź in qualitative terms.…”

Urban Day-to-Day Travel and Its Development in an Information Environment: A Review

“…Liu et al have discussed the effective information publishing strategy in the return to work (RTW) stage and have quantified the impact of information publishing on different types of travel demand. They found the phenomenon that private car travel demand recovered faster than public transportation travel demand when the urban transport system was fully opened to the public after the COVID-19 pandemic [183].…”

Social and spatial heterogeneities in COVID-19 impacts on individual's metro use: A big-data driven causality inference

“…By contrast, some studies have highlighted metro use responses to the recovery from post-COVID-19 or the relaxation of mobility controls. Some have found that metro ridership declines rapidly during the restriction period (to 50%–80% of pre-pandemic levels) but rebounds slowly in the return-to-work period (reaching up to 80%–90%), while car travel reaches 90%–100% of its pre-pandemic levels ( Liu et al, 2022 ). There is a potential trend of modal shift from metro transit to private car and ride-hailing at the recovery stage, as found in surveys conducted in the cities of Canada, China, and the U. S. ( Liu et al, 2022 ; Meredith-Karam et al, 2021 ; Parker et al, 2021 ).…”