Impact of COVID-19 on Urban Mobility during Post-Epidemic Period in Megacities: From the Perspectives of Taxi Travel and Social Vitality

Abstract

The prevention and control of COVID-19 in megacities is under large pressure because of tens of millions and high-density populations. The majority of epidemic prevention and control policies implemented focused on travel restrictions, which severely affected urban mobility during the epidemic. Considering the impacts of epidemic and associated control policies, this study analyzes the relationship between COVID-19, travel of residents, Point of Interest (POI), and social activities from the perspective of taxi travel. First, changes in the characteristics of taxi trips at different periods were analyzed. Next, the relationship between POIs and taxi travels was established by the Geographic Information System (GIS) method, and the spatial lag model (SLM) was introduced to explore the changes in taxi travel driving force. Then, a social activities recovery level evaluation model was proposed based on the taxi travel datasets to evaluate the recovery of social activities. The results demonstrated that the number of taxi trips dropped sharply, and the travel speed, travel time, and spatial distribution of taxi trips had been significantly influenced during the epidemic period. The spatial correlation between taxi trips was gradually weakened after the outbreak of the epidemic, and the consumption travel demand of people significantly decreased while the travel demand for community life increased dramatically. The evaluation score of social activity is increased from 8.12 to 74.43 during the post-epidemic period, which may take 3–6 months to be fully recovered as a normal period. Results and models proposed in this study may provide references for the optimization of epidemic control policies and recovery of public transport in megacities during the post-epidemic period.

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

“…Spatial statistics have mainly been used to analyze the socioeconomic and demographic risk factors of COVID‐19 (Iyanda et al., 2020 ; Sannigrahi, Pilla, Basu, Basu, & Molter, 2020 ; Sun, Matthews, Yang, & Hu, 2020 ; Urban & Nakada, 2021 ). Air quality (Maiti et al., 2020 ; Zulkarnain & Ramadani, 2020 ), health infrastructure (Mollalo, Vahedi, & Rivera, 2020 ), and mobility (Nian et al., 2020 ) have also been examined and linked to COVID‐19 risk and transmission. We have seen an influx of both spatial and spatiotemporal statistical methods applied to COVID‐19, with many studies utilizing the results to produce risk and social vulnerability maps.…”

“…If the residuals of an OLS model are spatially autocorrelated, then it is appropriate to use spatial regression‐based methods (Delmelle, Hagenlocher, Kienberger, & Cases, 2016 ). For example, a spatial lag model (SLM) can be used to examine how events at a location influence similar events in surrounding locations (i.e., spatial interaction); and a spatial error model (SEM) can be applied to account for autocorrelation of the residuals (Iyanda et al., 2020 ; Maiti et al., 2020 ; Mollalo, Vahedi, et al, 2020 ; Nian et al., 2020 ; Sannigrahi et al., 2020 ; Sun, Di, Sprigg, Tong, & Casal, 2020 ; Urban & Nakada, 2021 ); see Table 1 . For COVID‐19, spatially combined autoregressive models (SAC) have also been used as a combination of the previous models to simultaneously consider spatial lag and spatial error parameters (Sun, Di, et al., 2020 ; Zulkarnain & Ramadani, 2020 ).…”

Interactive effects between travel behaviour and COVID-19: a questionnaire study

“…In addition, Paradya et al [ 18 ] developed three models separately, pooled OLS, random-effect model and first-difference regression, to estimated going-out frequency. Changes in the characteristics of taxi trips at different periods were analysed by Nian et al [ 19 ], and a sharp drop of the number of taxi trips was found.…”

Evaluating the social impact of COVID-19 with a big data approach

“…By analyzing the taxi travel data set, the impact of COVID-19 on urban mobility is evaluated from the perspective of taxi travel and social vitality [6]. The taxi travel volume decreases significantly, while the travel speed, travel time and the spatial distribution of taxi trips are significantly affected by the epidemic situation, and social vitality may take 3-6 months as a normal period to fully recover [6]. COVID-19 led to a drop in the demand for transport services, including city public transport.…”