Impacts of COVID-19 pandemic on user behaviors and environmental benefits of bike sharing: A big-data analysis

Shang, Wen-Long; Chen, Jinyu; Bi, Huibo; Sui, Yi; Chen, Yanyan; Yu, Haitao

doi:10.1016/j.apenergy.2020.116429

Cited by 170 publications

(80 citation statements)

References 57 publications

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“…At the same time, socially acceptable travel options that comply with social distancing measures while reducing CO 2 emissions must be scaled up. In this respect, improving the safety and infrastructure of active transport modes could help turn the pandemic into an opportunity [19] , [53] . We highlight that in the focus countries 95% of all COVID-19 cycling infrastructures had been implemented.…”

Section: Discussionmentioning

confidence: 99%

Implications of COVID-19 lockdowns on surface passenger mobility and related CO2 emission changes in Europe

2021

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The coronavirus pandemic has severely affected our daily lives, with direct consequences on passenger transport. This in turn has strongly impacted the energy demand of the transport sector and associated CO 2 emissions. We analyse near real-time passenger mobility and related emission trends in Europe between 21 January and 21 September 2020. We compiled a dataset of country-, sector- and lockdown- specific values, representing daily activity changes in private, public, and active passenger transport. In the aggregate, surface passenger transport emissions fell by 11.2% corresponding to 40.3 MtCO 2 in Europe. This decline was predominantly due to the reduction of private passenger transport in five European countries (France, Germany, Italy, Spain, and the UK). During the first lockdown in April 2020, CO 2 emissions from surface passenger transport declined by 50% in Europe, resulting in a 7.1% reduction in total CO 2 emissions. After April 2020, private passenger travel recovered rapidly, while public passenger flows remained low. Solely prompted by the private sector, a rebound in total emissions and surface passenger transport emissions of 1.5% and 10.7%, respectively, was estimated at the end of the study period. The resulting situation of increased private and decreased public passenger transport is in contradiction to major climate goals, and without reversing these trends, emission reductions, as stated in the European Green Deal are unlikely to be achieved. Our study provides an analysis based on a detailed and timely set of data of surface passenger transport and points to options to grasp the momentum for innovative changes in passenger mobility.

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Section: Discussionmentioning

confidence: 99%

Implications of COVID-19 lockdowns on surface passenger mobility and related CO2 emission changes in Europe

2021

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“…Since the outbreak of COVID-19, researchers worldwide have been carrying out a lot of research works on it. These researches can be mainly divided into the following six categories: (1) to study the impact of COVID-19 on human physical and mental health from a biomedical perspective ( Tsamakis et al, 2020 , Xiong et al, 2020 , Pascoal et al, 2021 ); (2) to study the impact of COVID-19 on human production, life, and social and economic development from a sociological perspective ( Takyi and Bentum-Ennin, 2020 , Qian et al, 2021 , Shang et al, 2021 , Beiderbeck et al, 2021 , Jiang et al, 2021 ); (3) to creatively propose new mathematical models or revise some existing models based on relevant data for predicting and analyzing the development of the epidemic in a specific area ( Vianello et al, 2021 , Willis et al, 2021 , Mun and Geng, 2021 , Al-qaness et al, 2021 , Manenti et al, 2020 , Hu et al, 2020 , Cao et al, 2020 , Mojjada et al, 2020 , Yang et al, 2020 ); (4) to analyze the spatial-temporal characteristics of the epidemic in a specific area ( Lv and Cheng, 2020 , Feng et al, 2020 ); (5) to explore related factors which may affect the development of the epidemic ( Hu et al, 2021 ); (6) to evaluate the effects of different epidemic prevention measures ( Leung et al, 2020 , Hasnain et al, 2020 ). In terms of the research purpose and content, the third, the fourth, and the fifth categories are more relevant to the work carried out in this paper.…”

Section: Related Workmentioning

confidence: 99%

Analysis on the spatio-temporal characteristics of COVID-19 in mainland China

Jin

Yang

et al. 2021

Process Safety and Environmental Protection

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COVID-19 has brought many unfavorable effects on humankind and taken away many lives. Only by understanding it more profoundly and comprehensively can it be soundly defeated. This paper is dedicated to studying the spatial-temporal characteristics of the epidemic development at the provincial-level in mainland China and the civic-level in Hubei Province. Moreover, a correlation analysis on the possible factors that cause the spatial differences in the epidemic's degree is conducted. After completing these works, three different methods are adopted to fit the daily-change tendencies of the number of confirmed cases in mainland China and Hubei Province. The three methods are the Logical Growth Model (LGM), Polynomial fitting, and Fully Connected Neural Network (FCNN). The analysis results on the spatial-temporal differences and their influencing factors show that: (1) The Chinese government has contained the domestic epidemic in early March 2020, indicating that the number of newly diagnosed cases has almost zero increase since then. (2) Throughout the entire mainland of China, effective manual intervention measures such as community isolation and urban isolation have significantly weakened the influence of the subconscious factors that may impact the spatial differences of the epidemic. (3) The classification results based on the number of confirmed cases also prove the effectiveness of the isolation measures adopted by the governments at all levels in China from another aspect. It is reflected in the small monthly grade changes (even no change) in the provinces of mainland China and the cities in Hubei Province during the study period. Based on the experimental results of curve-fitting and considering the time cost and goodness of fit comprehensively, the Polynomial( Degree = 18) model is recommended in this paper for fitting the daily-change tendency of the number of confirmed cases.

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“…The outbreak has changed people's behavior to choose healthier transportation. Shang et al [14] exposed that the use of bikes increases environmental benefits regarding emission reduction and energy conservation.…”

Section: Environmentmentioning

confidence: 99%

“…COVID-19 has increased the anxiety of many people and can cause mental health problems. Studies on COVID-19 discussed social psychology, examining people's behavior in social situations and their capability to adapt to certain conditions' social environments [14]. Research topics in social psychology covered in the past studies include the relationship between trust and the presence of infectious disease [70]; psychological needs and their satisfaction level during the pandemic [71]; the effect of fear and collectivism on the public prevention against COVID-19 [72]; peoples' preferences to protect the environment they live in [73].…”

Section: Social Lifementioning

confidence: 99%

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A Survey on Big Data Research in Fighting COVID-19: Contributions and Techniques

Riswantini

Nugraheni

Munandar

et al. 2021

Preprint

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COVID-19 has induced many problems in various sectors of life for humanity around the world. After one year of pandemic, many studies have been carried out in exposing various technology innovations and applications to combat the coronavirus that has killed more people than most. The pandemic has accelerated the use of Big Data technology to mitigate the threats of COVID-19. This survey aims to explore the Big Data research for COVID-19. We collected and analyzed the relevant academic articles to identify how Big Data technology can cover the challenges faced in overcoming the pandemic. In determining the research areas addressed by the past studies, we highlight the technology contributions to five major areas of healthcare, social life, government policy, business and management, and the environment. We discuss how analytical techniques of machine learning, deep learning, statistics, and mathematics can solve pandemic issues. The Big Data research for COVID-19 used a wide variety of data sources available publicly or privately. At the end of the discussion, we present the data source used in the past studies encompassing government official data, institutional service data, IoT generated data, online media data, and open data. We hope that this survey will clarify the role of Big Data technology in enhancing the research for COVID-19.

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Impacts of COVID-19 pandemic on user behaviors and environmental benefits of bike sharing: A big-data analysis

Cited by 170 publications

References 57 publications

Implications of COVID-19 lockdowns on surface passenger mobility and related CO2 emission changes in Europe

Implications of COVID-19 lockdowns on surface passenger mobility and related CO2 emission changes in Europe

Analysis on the spatio-temporal characteristics of COVID-19 in mainland China

A Survey on Big Data Research in Fighting COVID-19: Contributions and Techniques

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