The impacts of the COVID-19 pandemic on multimodal human mobility in London: A perspective of decarbonizing transport

ABSTRACT

Decarbonizing transport is one of the core tasks for achieving Net Zero targets, but the COVID-19 pandemic disrupts human mobility and the established transport development strategies. Although existing research has explored the relationship between virus transmission, human mobility, and restrictions policies, few have studied the responses of multimodal human mobility to the pandemic and their impacts on the achievement of decarbonizing transport. This paper employs 32 consecutive biweekly observations of mobile phone application data to understand the influences of the pandemics on multimodal human mobility from February 2020 to April 2021 in London. We here illustrate that multimodal travel behavior and traffic flows significant changed after the pandemic and related lockdowns, but the decline or recovery varies across different travel modes and lockdowns. The car mode has shown the most resilience throughout the pandemic, but the travel modes in the public transit sector were hit hard. Cycle and walk modes remained high at the beginning of the pandemic, but the trend did not continue as the pandemic developed and the season changed. Our findings suggest that the COVID-19 pandemic brought more challenges to travel mode shifting and the achievement of decarbonizing transport rather than opportunities. This analysis will assist transport authorities to optimize the established transport policies and to redistribute limited resources for accelerating the achievement of decarbonizing transport.

KEYWORDS:

• COVID-19 pandemic
• multimode
• human mobility
• decarbonizing transport

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to the privacy issues.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10095020.2022.2122876.

Additional information

Notes on contributors

Xianghui Zhang

Xianghui Zhang is a PhD student at SpaceTimeLab, University College London. His research interests are urban computing, human mobility, and transport policy.

Tao Cheng

Tao Cheng is a Professor in GeoInformatics and the Director of SpaceTimeLab at University College London. Her research interests span Space-Time AI, network complexity, and urban analytics.