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Author:

Shang, Wen-Long (Shang, Wen-Long.) | Chen, Jinyu (Chen, Jinyu.) | Bi, Huibo (Bi, Huibo.) | Sui, Yi (Sui, Yi.) | Chen, Yanyan (Chen, Yanyan.) | Yu, Haitao (Yu, Haitao.)

Indexed by:

SSCI EI Scopus SCIE PubMed

Abstract:

The COVID-19 pandemic spreads rapidly around the world, and has given rise to huge impacts on all aspects of human society. This study utilizes big data techniques to analyze the impacts of COVID-19 on the user behaviors and environmental benefits of bike sharing. In this study, a novel method is proposed to calculate the trip distances and trajectories via a python package OSMnx so as to accurately estimate the environmental benefits of bike sharing. In addition, we employ the topological indices arising from complex network theory to quantitatively analyze the transformation of user behavior pattern of bike sharing during the COVID-19 pandemic. The results show that this pandemic has impacted the user behaviors and environmental benefits of bike sharing in Beijing significantly. During the pandemic, the estimated reductions of energy consumption and emissions on 6th Feb decreased to approximately 1 in 17 of those on a normal day, and the environmental benefits at most recovered to 70% of those in normal days. The impacts of COVID-19 on the environmental benefits in different districts are different. Furthermore, the decline of average strength and strength distribution obeying exponential distribution but with different slope rates suggests that people are less likely to take bike sharing to the places where were popular before. The pandemic has also increased the average trip time of bike sharing. Our research may facilitate the understanding of the impacts of COVID-19 pandemic on our society and environment, and also provide clues to adapt to this unprecedented pandemic so as to respond to similar events in the future.

Keyword:

COVID-19 Big-data Environmental benefits Bike sharing User behaviors

Author Community:

  • [ 1 ] [Shang, Wen-Long]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 2 ] [Bi, Huibo]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Chen, Yanyan]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 4 ] [Chen, Jinyu]Univ Tokyo, Ctr Spatial Informat Sci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778568, Japan
  • [ 5 ] [Sui, Yi]Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Peoples R China
  • [ 6 ] [Yu, Haitao]Beijing Transportat Informat Ctr, Beijing 100161, Peoples R China
  • [ 7 ] [Yu, Haitao]Beijing Key Lab Comprehens Traff Operat Monitorin, Beijing 100161, Peoples R China

Reprint Author's Address:

  • [Bi, Huibo]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China

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Source :

APPLIED ENERGY

ISSN: 0306-2619

Year: 2021

Volume: 285

1 1 . 2 0 0

JCR@2022

ESI Discipline: ENGINEERING;

ESI HC Threshold:87

JCR Journal Grade:1

Cited Count:

WoS CC Cited Count: 12

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 18 Unfold All

  • 2024-11
  • 2024-11
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  • 2023-11
  • 2023-9
  • 2023-7
  • 2023-5
  • 2023-3
  • 2023-1
  • 2022-11
  • 2022-9
  • 2022-7
  • 2022-5

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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