Unraveling the dynamic impacts of COVID-19 on metro ridership: An empirical analysis of Beijing and Shanghai, China

Investigating the potential of aggregated mobility indices for inferring public transport ridership changes

Aggregated mobility indices (AMIs) derived from information and communications technologies have recently emerged as a new data source for transport planners, with particular value during periods of major disturbances or when other sources of mobility data are scarce. Particularly, indices estimated on the aggregate user concentration in public transport (PT) hubs based on GPS of smartphones, or the number of PT navigation queries in smartphone applications have been used as proxies for the temporal changes in PT aggregate demand levels. Despite the popularity of these indices, it remains largely untested whether they can provide a reasonable characterisation of actual PT ridership changes. This study aims to address this research gap by investigating the reliability of using AMIs for inferring PT ridership changes by offering the first rigorous benchmarking between them and ridership data derived from smart card validations and tickets. For the comparison, we use monthly and daily ridership data from 12 cities worldwide and two AMIs shared globally by Google and Apple during periods of major change in 2020-22. We also explore the complementary role of AMIs on traditional ridership data. The comparative analysis revealed that the index based on human mobility (Google) exhibited a notable alignment with the trends reported by ridership data and performed better than the one based on PT queries (Apple). Our results differ from previous studies by showing that AMIs performed considerably better for similar periods. This finding highlights the huge relevance of dealing with methodological differences in datasets before comparing. Moreover, we demonstrated that AMIs can also complement data from smart card records when ticketing is missing or of doubtful quality. The outcomes of this study are particularly relevant for cities of developing countries, which usually have limited data to analyse their PT ridership, and AMIs may offer an attractive alternative.

A multiple mediation analysis to untangle the impacts of COVID-19 on nationwide bus ridership in the United States

The COVID-19 pandemic has resulted in major consequences for many aspects of human life and the broader economy. Many transportation modes were severely impacted, including public transportation. During the early months of the pandemic in 2020, transit ridership dropped to unprecedented levels. Even by the end of 2022, bus ridership in the United States had not recovered to pre-pandemic levels. Despite the longstanding effects on public transportation, the direct and indirect impacts of the COVID-19 pandemic on bus ridership are largely unknown. In the context of this study, the direct impact refers to a change in travel behavior (i.e., due to the increased spread of COVID-19), while the indirect impact refers to reduced ridership due to factors such as lower employment or increased teleworking. This study proposes a framework to explore the drivers of transit ridership declines during COVID-19. The method is a multiple mediation analysis to estimate the monthly direct and indirect impacts of COVID-19 on bus ridership from March 2020 to December 2021. The results of this study revealed that three mediators (employment, telework, and people relocating) mediated about 13% to 38% of the total decline in bus ridership during the analysis period. The multiple mediation approach used in this study could be applied in many other transportation applications.

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

While mobility intervention policies implemented during the early stages of the COVID-19 outbreak had a significant impact on public transit use, few studies have investigated the individual-level responses in metro transit riding behaviors. Using long time-series cellphone big data from frequent metro users in Shenzhen, China, we developed a quasi-experimental interrupted time series (ITS) design to estimate the treatment effects of mobility intervention policies on people's daily shares of metro transit use (SMU). The results indicate that the first-level emergency response (FLR) and the public transit restriction (PTR) policy yielded abrupt drops in SMU of 8.0% and 17.6%, respectively, whereas the return-to-work (RTW) order had an immediate recovery effect of 14.5%. The effect of the FLR is time-decreasing while those effects of the PTR and the RTW are time-increasing. Females and elderly people living in neighborhoods near the city center with low population density and fewer transit stations are more adaptable to policy interventions for reducing SMUs, while the recovery effect of RTW is relatively low for the elderly living in less mixed-use neighborhoods with reduced transit service. These findings can help policymakers design more socially- and spatially-precise and equity mobility intervention policies during a pandemic.

Metro travel and perceived COVID-19 infection risks: A case study of Hong Kong

The COVID-19 pandemic has exerted unprecedented impacts on travel behaviors because of people's increased health precautions and the presence of various COVID-19 containment measures. However, little research has explored whether and how people changed their travel with respect to their perceived local infection risks across space and time. In this article, we relate elasticity and resilience thinking to the changes in metro travel and perceived infection risks at the station or community level over time. Using empirical data from Hong Kong, we measure a metro station's elasticity as the ratio of changes in its average trip length to the COVID-19 cases' footprints around that station. We regard those footprints as a proxy for people's perceived infection risks when making trips to that station. To explore influencing factors on travel in the ups and downs of perceived infection risks, we classify stations based on their elasticity values and examine the association between stations' elasticities and characteristics of stations and their served communities. The findings show that stations varied in elasticity values across space and different surges of the local pandemic. The elasticity of stations can be predicted by socio-demographics and physical attributes of station areas. Stations serving a larger percentage of population with higher education degrees and certain occupations observed more pronounced trip length decrease for the same level of perceived infection risks. The number of parking spaces and retail facilities significantly explained variations in stations' elasticity. The results provide references on crisis management and resilience improvement amid and post COVID-19.

Systematic literature review on impacts of COVID-19 pandemic and corresponding measures on mobility

The unprecedented COVID-19 outbreak has significantly influenced our daily life, and COVID-19's spread is inevitably associated with human mobility. Given the pandemic's severity and extent of spread, a timely and comprehensive synthesis of the current state of research is needed to understand the pandemic's impact on human mobility and corresponding government measures. This study examined the relevant literature published to the present (March 2023), identified research trends, and conducted a systematic review of evidence regarding transport's response to COVID-19. We identified key research agendas and synthesized the results, examining: (1) mobility changes by transport modes analyzed regardless of government policy implementation, using empirical data and survey data; (2) the effect of diverse government interventions to reduce mobility and limit COVID-19 spread, and controversial issues on travel restriction policy effects; and (3) future research issues. The findings showed a strong relationship between the pandemic and mobility, with significant impacts on decreased overall mobility, a remarkable drop in transit ridership, changes in travel behavior, and improved traffic safety. Government implemented various non-pharmaceutical countermeasures, such as city lockdowns, travel restrictions, and social distancing. Many studies showed such interventions were effective. However, some researchers reported inconsistent outcomes. This review provides urban and transport planners with valuable insights to facilitate better preparation for future health emergencies that affect transportation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11116-023-10392-2.

On the bumpy road to recovery: resilience of public transport ridership during COVID-19 in 15 European cities

COVID-19 has a dramatic impact on the use of public transport (PT). Most European cities report a decline in PT use during 2020 and 2021. Nevertheless, not all cities report similar decline patterns or comparable resilience paths. We investigate the resilience patterns of PT use during 2020 and 2021 in 15 European cities from 11 different countries using clustering and regression analysis of data originating from Google Mobility Reports, the Oxford Policy Stringency Tracker, and COVID-19 reports. Results highlight the variety of resilience patterns of PT use in these 15 cities. These patterns vary in time and space. PT use in some cities recovered faster and more significantly than in others. Findings also suggest that changes in retail and recreational routines had the highest impact on the resilience of PT use in most cities. Changes in workplace routines are also important, but to a lesser degree. The impact of policy stringency on PT use is significant, but less consistent between the 15 cities.

The Impact of Urban Rail Transit Epidemic Prevention Measures on Passengers' Safety Perception

In 2020, COVID-19 triggered concern about the safety of public transport. To meet passengers' expectations regarding safety, the public transport department has stepped up its pandemic prevention services. Some prevention services require passengers to follow mandatory requirements. However, whether and to what extent these requirements affect passenger satisfaction with public transportation services remains unclear. This study aims to construct an integrated framework to explore the direct and indirect relationships between four constructs (regular services quality, pandemic prevention service, psychological distance, and safety perception) and passengers' satisfaction in the context of urban rail transit services. Based on survey data collected from 500 passengers on the Shanghai Metro, this paper examines the relationships between routine service, pandemic prevention measures, safety perceptions, and satisfaction with the service. The results from the structural equation model indicate that routine service (0.608), pandemic prevention measures (0.56), and safety perception (0.05) have positive effects on passenger satisfaction. Psychological distance negatively impacts safety perception (-0.949) and has indirect effects on passenger satisfaction. Further, in order to identify the service improvements that public transportation departments should focus on, we use the three-factor theory to identify the services that should be improved: Basic factors, such as "punctual arrival of metros", "treatment of harmful garbage", "increasing frequency of platform disinfection", and "measurement of station temperature" should be treated as the first priority. As the second improvement priority, "the planning of metro stations can accommodate my travel scope" can be considered. Last, public transportation departments can enhance the exciting factor by installing "metro entrance signs" when resources are available.