Determining factors affecting public bike ridership and its spatial change before and after COVID-19

Kim, Jin Man; Lee, Sugie

doi:10.1016/j.tbs.2022.11.002

Cited by 16 publications

(10 citation statements)

References 50 publications

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“…This is consistent with findings in many other cities worldwide ( Bucsky, 2020 ; Heydari et al, 2021 ; Hu et al, 2021 ; J. Kim & Lee, 2023 ; Orro et al, 2020 ; Padmanabhan et al, 2021 ; Shang et al, 2021 ; Teixeira & Lopes, 2020 ; Xin et al, 2022 ). The reduction observed in Valencia was 40 % throughout the post-outbreak year, which is similar to the 40 % observed in Beijing ( Chai et al, 2021 ) approximately 32.35 % reported in Chicago ( Hu et al, 2021 ).…”

Section: Discussionsupporting

confidence: 92%

“…However, follow-up recovery was observed compared with other modes of transport, although it did not always reach pre-pandemic levels, especially when BSS services re-opened or restrictions were relaxed ( Bucsky, 2020 ; Heydari et al, 2021 ; Hu et al, 2021 ; J. Kim & Lee, 2023 ; Orro et al, 2020 ; Padmanabhan et al, 2021 ; Shang et al, 2021 ; Teixeira & Lopes, 2020 ; Xin et al, 2022 ). In addition, several studies, which used different data-gathering methods, agreed that BSS was more resilient than other forms of public mobility due to its versatility and flexibility, which enable it to recover easily from changes or shocks ( Mattsson & Jenelius, 2015 ).…”

Section: Bsss and The Covid-19 Pandemicmentioning

confidence: 91%

“…Other studies in Chicago and Seoul have noted that open spaces and green areas had a statistically significant positive impact on bike-sharing usage, while industrial areas had a negative relationship ( Hu et al, 2021 ; Jiao et al, 2022 ). According to Kim and Lee (2023) , outdoor spaces, accessibility to subway stations and bike lanes have significantly increased bike-sharing ridership since the COVID-19 outbreak. These findings highlight the importance of expanding public bike infrastructure to promote BSS and cycling, as found in other studies ( Berezvai, 2022 ; Bustamante et al, 2022 ; Xin et al, 2022 ).…”

Section: Bsss and The Covid-19 Pandemicmentioning

confidence: 99%

See 2 more Smart Citations

Who changed and who maintained their urban bike-sharing mobility after the COVID-19 outbreak? A within-subjects study

Seifert

Pellicer-Chenoll

Antón-González

et al. 2023

Cities

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Section: Discussionsupporting

confidence: 92%

Section: Bsss and The Covid-19 Pandemicmentioning

confidence: 91%

Section: Bsss and The Covid-19 Pandemicmentioning

confidence: 99%

See 1 more Smart Citation

Who changed and who maintained their urban bike-sharing mobility after the COVID-19 outbreak? A within-subjects study

Seifert

Pellicer-Chenoll

Antón-González

et al. 2023

Cities

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“…Conversely, leisure facilities, especially those with aquatic features, showed a positive correlation with cycling distance. Kim and Lee (2023) and Jiao et al (2022) similarly concluded that leisure facilities, like parks, can increase cycling distance, while areas with higher land-use intensity and building density result in decreased cycling distance. Similar findings were reported by Kabak et al (2018) and Wang et al (2018), suggesting that parks, convenience service areas, transport hubs, and residential areas all contribute to extended cycling distances.…”

Section: Literature Reviewmentioning

confidence: 99%

The nonlinear impact of cycling environment on bicycle distance: A perspective combining objective and perceptual dimensions

Zhang,

2024

JTLU

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Extending cycling distances is crucial for sustainable urban transport development and plays a role in encouraging the shift from motorized vehicles to public transport. However, there is a lack of research examining the combined impacts of both objective and perceived aspects of the cycling environment on cycling distance, and the existence of threshold effects remains unclear. This study uses 2019 cycling data from Shenzhen, China, employing the XGBoost algorithm to uncover the relative importance and thresholds of objective and perceived factors in the cycling environment. The results indicate that population density (24.8%), road network density (15.2%), the proportion of recreational facilities (9.1%), perceived accessibility (8.0%), and comfort (8.6%) hold high relative importance in predicting cycling distance. Also, maintaining road network density between 3 to 6 km/km2 and increasing the population density to exceed 22,000 people/km2 proves effective in extending cycling distances. Land use demonstrates a threshold effect, with cycling distances increasing when the recreational facilities share exceeds 8%, transport facilities share remains below 25%, and commercial facilities share stays below 30%. Perceived metrics exhibit a clear threshold effect. The study identifies that perceived safety indicates a psychological bottleneck in increasing cycling distance. Perceived accessibility is positively correlated with cycling distance when accessibility is at a low level, while comfort shows a positive correlation with cycling distance when comfort is at a high level. These findings can contribute to refining land planning and prioritizing resource allocation for organizations aiming to promote non-motorized travel and design bicycle-friendly environments.

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“…On a macro level, analyzing the various factors in uencing transportation ridership is a signi cant challenge for transportation scientists and policymakers (Liu, 2019;Kim et al, 2023). Existing research has found that subway ridership levels in different contexts are associated with a variety of factors such as socio-economic and demographic characteristics, land use features, functional area attributes, and the impact of major emergencies (Yao, 2020).…”

Section: Factors Affecting Transportation Ridershipmentioning

confidence: 99%

Urban Transport Dynamics During COVID-19: Examining Subway Ridership and Road Congestion Trends in Beijing Amidst Varied Built Environments

Cui,

Xue

2024

Preprint

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This study delves into the profound impact of the COVID-19 pandemic on subway ridership and road congestion in Beijing, with a specific focus on the moderating role of the built environment. Utilizing panel data regression models, our analysis encompasses the daily average subway ridership at 157 stations over 366 days, alongside corresponding average congestion indices. The study integrates a comprehensive set of variables including COVID-19 case numbers, public search trends, various emergency response levels, and control variables such as the density of work, residence, and entertainment tourism areas. Our findings reveal a significant downturn in subway ridership and a decrease in road congestion during the pandemic, with these effects being more pronounced in high-density residential and commercial areas. The study indicates that the built environment plays a crucial role in shaping travel patterns during such a public health crisis. Specifically, areas with high work and residential density experienced a substantial reduction in subway usage, while regions with elevated entertainment and tourism density witnessed an increase in private vehicle usage. The temporal analysis of the data presents further insights. The initial stages of the pandemic saw a steep decline in subway ridership, suggesting an immediate reaction to the outbreak and associated restrictions. As the pandemic progressed, there was evidence of gradual adaptation in travel behavior, with a slow recovery in subway ridership and changes in road congestion patterns. Moreover, the study highlights the varying impact of COVID-19 across different stages of the pandemic. In the early phase, stringent lockdown measures and high public fear significantly influenced mobility patterns. However, in the later stages, as the public adapted to the ‘new normal,’ there was a notable shift in travel preferences, with a gradual return to public transit and changes in congestion levels. Overall, this research provides comprehensive insights into how a global health crisis can reshape urban transportation dynamics. It underscores the necessity for adaptive urban planning and policy-making that can effectively respond to such unprecedented challenges, taking into consideration the diverse needs of different urban areas and stages of public health emergencies.

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Determining factors affecting public bike ridership and its spatial change before and after COVID-19

Cited by 16 publications

References 50 publications

Who changed and who maintained their urban bike-sharing mobility after the COVID-19 outbreak? A within-subjects study

Who changed and who maintained their urban bike-sharing mobility after the COVID-19 outbreak? A within-subjects study

The nonlinear impact of cycling environment on bicycle distance: A perspective combining objective and perceptual dimensions

Urban Transport Dynamics During COVID-19: Examining Subway Ridership and Road Congestion Trends in Beijing Amidst Varied Built Environments

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