How did micro-mobility change in response to COVID-19 pandemic? A case study based on spatial-temporal-semantic analytics

Li, Aoyong; Zhao, Pengxiang; He, Haitao; Mansourian, Ali; Axhausen, Kay W.

doi:10.1016/j.compenvurbsys.2021.101703

Cited by 100 publications

(67 citation statements)

References 38 publications

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“…The results are shown in this study: (a) For all the 368 districts in Taiwan with high human mobility and low vaccine rates before the emergence of B.1.1.7, even the partial relaxation of the entry policy for specific imported groups, during the process of reopening, could lead to the emergence and rapid spatiotemporal 1.7. This factor also supports the findings of some previous studies based on the relationship between mobility and COVID-19 transmission (Chen et al, 2021;Jia et al, 2020;Levin et al, 2021;Li et al, 2021;Nouvellet et al, 2021;Sachak-Patwa et al, 2021). The above conclusion can act as a guide to any region considering reopening to formulate precise entry measures and hence prevent the emergence of new variants in that particular region and thereby cutting possible networks of spread.…”

Section: Discussionsupporting

confidence: 89%

Tracking and Controlling the Spatiotemporal Spread of SARS‐CoV‐2 Lineage B.1.1.7 in COVID‐19 Reopenings

et al. 2021

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The SARS-CoV-2 lineage B.1.1.7 (Alpha) has spread to 114 countries around the world since it was first detected in the UK in September 2020 (P. Wang, Nair, et al., 2021). Some studies have shown that B.1.1.7 is not only 43%-90% (95% CI 38-130) more transmissible than preexisting variants (Jewell, 2021;Walensky et al., 2021) but also causes more severe illness (Davies et al., 2021;Patone et al., 2021). With the accelerated vaccination, the world is gradually entering a regular prevention and control stage (Pham et al., 2021) with some regions preparing to reopen to the world (Chang et al., 2021;Zhao et al., 2021). In order to assist these regions to effectively prevent the emergence of new variants in the reopening process, it became crucial to explore the nonpharmaceutical intervention (NPI) measures that affect the emergence and spread of SARS-CoV-2 variants such as B.1.1.7 and thereby, first learn in general how to effectively control the spatiotemporal spread of SARS-CoV-2 variants. Taiwan, one of the regions where B.1.1.7 appeared during the reopening process (Shonchoy et al., 2021;Tan, 2021;Yu et al., 2021), is chosen regarding the exploration of the spatial dynamics of the B.1.1.7 spread and investigation with the aim of exploring relevant data and thereby finding the potential means to effectively control the spatiotemporal spread of B.1.1.7, potentially by the integration of vaccination and NPI measures.

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Section: Discussionsupporting

confidence: 89%

Tracking and Controlling the Spatiotemporal Spread of SARS‐CoV‐2 Lineage B.1.1.7 in COVID‐19 Reopenings

et al. 2021

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“…Previous studies on understanding the effect of the outbreak of communicable diseases on bike-sharing schemes are relatively limited [ 8 , 12 , 33 – 41 ] A study conducted by Wang and Noland [ 12 ] examined the effect of the lockdown and the subsequent phases of reopening on Citi Bike in New York, analysing two years of data, 2019 and 2020. The authors used a Prais-Winsten regression model that accommodates serial correlation given the time-series nature of their data.…”

Section: Introductionmentioning

confidence: 99%

“…They found that the demand for the Citi Bike system decreased sharply after the lockdown, but it started to return normal afterwards. Another recent study conducted by Li et al [ 41 ] found that travellers, in Zurich, preferred to use micro-mobility services (including bike-sharing services) during the COVID-19 pandemic, and that these services were used for longer trips. Chibwe et al [ 8 ] found that the first national lockdown, introduced in March 2020, in England decreased the demand for the London bike-sharing system by around 22%.…”

Section: Introductionmentioning

confidence: 99%

Effect of the COVID-19 pandemic on bike-sharing demand and hire time: Evidence from Santander Cycles in London

2021

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The COVID-19 pandemic has been influencing travel behaviour in many urban areas around the world since the beginning of 2020. As a consequence, bike-sharing schemes have been affected—partly due to the change in travel demand and behaviour as well as a shift from public transit. This study estimates the varying effect of the COVID-19 pandemic on the London bike-sharing system (Santander Cycles) over the period March–December 2020. We employed a Bayesian second-order random walk time-series model to account for temporal correlation in the data. We compared the observed number of cycle hires and hire time with their respective counterfactuals (what would have been if the pandemic had not happened) to estimate the magnitude of the change caused by the pandemic. The results indicated that following a reduction in cycle hires in March and April 2020, the demand rebounded from May 2020, remaining in the expected range of what would have been if the pandemic had not occurred. This could indicate the resiliency of Santander Cycles. With respect to hire time, an important increase occurred in April, May, and June 2020, indicating that bikes were hired for longer trips, perhaps partly due to a shift from public transit.

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“…during the COVID-19 lockdown and the “new normality” phases. Finally, the impact of the pandemic on micro-mobility services was analyzed in [ 36 ], showing that trips remarkably decreased during the lockdown period in Zurich.…”

Section: Related Workmentioning

confidence: 99%

Characterization of COVID-19’s Impact on Mobility and Short-Term Prediction of Public Transport Demand in a Mid-Size City in Spain

González

Wilby

Díaz

et al. 2021

Sensors

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COVID-19 has dramatically struck each section of our society: health, economy, employment, and mobility. This work presents a data-driven characterization of the impact of COVID-19 pandemic on public and private mobility in a mid-size city in Spain (Fuenlabrada). Our analysis used real data collected from the public transport smart card system and a Bluetooth traffic monitoring network, from February to September 2020, thus covering relevant phases of the pandemic. Our results show that, at the peak of the pandemic, public and private mobility dramatically decreased to 95% and 86% of their pre-COVID-19 values, after which the latter experienced a faster recovery. In addition, our analysis of daily patterns evidenced a clear change in the behavior of users towards mobility during the different phases of the pandemic. Based on these findings, we developed short-term predictors of future public transport demand to provide operators and mobility managers with accurate information to optimize their service and avoid crowded areas. Our prediction model achieved a high performance for pre- and post-state-of-alarm phases. Consequently, this work contributes to enlarging the knowledge about the impact of pandemic on mobility, providing a deep analysis about how it affected each transport mode in a mid-size city.

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How did micro-mobility change in response to COVID-19 pandemic? A case study based on spatial-temporal-semantic analytics

Cited by 100 publications

References 38 publications

Tracking and Controlling the Spatiotemporal Spread of SARS‐CoV‐2 Lineage B.1.1.7 in COVID‐19 Reopenings

Tracking and Controlling the Spatiotemporal Spread of SARS‐CoV‐2 Lineage B.1.1.7 in COVID‐19 Reopenings

Effect of the COVID-19 pandemic on bike-sharing demand and hire time: Evidence from Santander Cycles in London

Characterization of COVID-19’s Impact on Mobility and Short-Term Prediction of Public Transport Demand in a Mid-Size City in Spain

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