ArticlePublic transport (Heidelberg, Germany)2022
Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data.
Article in Public transport (Heidelberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Inferring the timing of individual mobility decisions from accommodation reservation data during the COVID-19 outbreak.Royal Society open science · 2025Article
- Social and spatial heterogeneities in COVID-19 impacts on individual's metro use: A big-data driven causality inference.Applied geography (Sevenoaks, England) · 2023Article
- Estimation of trip purposes in public transport during the COVID-19 pandemic: The case of Santiago, Chile.Journal of transport geography · 2023Article
- Who is returning to public transport for non-work trips after COVID-19? Evidence from older citizens' smart cards in the UK's second largest city region.Journal of transport geography · 2023Article
- To increase or to decrease the price? Managing public transport queues during COVID-19 in the presence of strategic commuters.Public transport (Heidelberg, Germany) · 2023Article
- The impact of COVID-19 and related containment measures on Bangkok's public transport ridership.Transportation research interdisciplinary perspectives · 2023Article
- Enhancing service quality of rural public transport during the COVID-19 pandemic: a novel fuzzy approach.Public transport (Heidelberg, Germany) · 2023Article
- Geovisualizing the changes in metro passenger flows of Kunming under the impact of COVID-19.Journal of transport geography · 2022Article
- Unraveling the dynamic impacts of COVID-19 on metro ridership: An empirical analysis of Beijing and Shanghai, China.Transport policy · 2022Article
- Data analytics during pandemics: a transportation and location planning perspective.Annals of operations research · 2022Article
- Robustness and disturbances in public transport.Public transport (Heidelberg, Germany) · 2022Article
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2 authors.
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Abstract
Modern public transit systems are often run with automated fare collection (AFC) systems in combination with smart cards. These systems passively collect massive amounts of detailed spatio-temporal trip data, thus opening up new possibilities for public transit planning and management as well as providing new insights for urban planners. We use smart card trip data from Taipei, Taiwan, to perform an in-depth analysis of spatio-temporal station-to-station metro trip patterns for a whole week divided into several time slices. Based on simple linear regression and line graphs, days of the week and times of the day with similar temporal passenger flow patterns are identified. We visualize magnitudes of passenger flow based on actual geography. By comparing flows for January to March 2019 and for January to March 2020, we look at changes in metro trips under the impact of the coronavirus pandemic (COVID-19) that caused a state of emergency around the globe in 2020. Our results show that metro usage under the impact of COVID-19 has not declined uniformly, but instead is both spatially and temporally highly heterogeneous.
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