Centre for Public Health, Queen's University Belfast, Royal Victoria Hospital, Institute of Clinical Sciences B, Belfast, UK.
Centre for Public Health, Queen's University Belfast, Royal Victoria Hospital, Institute of Clinical Sciences B, Belfast, UK.
Lancet Public Health. 2024 Nov;9(11):e896-e906. doi: 10.1016/S2468-2667(24)00222-6.
During the COVID-19 pandemic, changes were seen in city mobility patterns around the world, including in active transportation (walking, cycling, micromobility, and public transit use), creating a unique opportunity for global public health lessons and action. We aimed to analyse a global natural experiment exploring city mobility patterns during the pandemic and how they related to the implementation of COVID-19-related policies.
We obtained data from Apple's Mobility Trends Reports on city mobility indexes for 296 cities from Jan 13, 2020 to Feb 4, 2022. Mobility indexes represented the frequency of Apple Maps queries for driving, walking, and public transit journeys relative to a baseline value of 100 for the pre-pandemic period (defined as Jan 13, 2020). City mobility index trajectories were plotted with stratification by country income level, transportation-related city type, population density, and COVID-19 pandemic severity (SARS-CoV-2 infection rate). We also synthesised global pandemic policies and recovery actions that promoted or restricted city mobility and active transportation (walking, cycling and micromobility, and public transit) using the Shifting Streets dataset. Additionally, a natural experiment on a global scale evaluated the effects of new active transportation policies on walking and public transit use in cities around the world. We used multivariable regression with a difference-in-difference (DID) analysis to explore whether the implementation of walking or public transit promotion policies affected mobility indexes, comparing cities with and without implementation of these policies in the pre-intervention period (Jan 27 to April 12, 2020) and post-intervention period (April 13 to June 28, 2020).
Based on city mobility index trajectories, we observed an overall decline in mobility indexes for walking, driving, and public transit at the beginning of the pandemic, but these values began to increase in April, 2020. Cities with lower population densities generally had higher driving and walking indexes than cities with higher population density, while cities with higher population densities had higher public transit indexes. Cities with higher pandemic severity generally had higher driving and walking indexes than cities with lower pandemic severity, while cities with lower pandemic severity had higher public transit indexes than other cities. We identified 587 policies in the dataset that had known implementation dates and were relevant to active transportation, which included 305 policies on walking, 321 on cycling and micromobility, and 143 on public transit, across 230 cities within 33 countries (19 high-income, 11 middle-income, and three low-income countries). In the global natural experiment (including 39 cities), implementation of policy interventions promoting walking was significantly associated with a higher absolute value of the walking index (DID coefficient 20·675 [95% CI 8·778-32·572]), whereas no such effect was seen for public transit-promoting policies (0·600 [-13·293 to 14·494]).
Our results suggest that the policies implemented to mitigate the COVID-19 pandemic were effective in changing city mobility patterns, especially increasing active transportation. Given the known benefits of active transportation, such policies could be maintained, expanded, and evaluated post pandemic. The discrepancy in the interventions between countries of different incomes highlights that changes to the infrastructure to prioritise safe walking, cycling, and easy access to public transit use could help with the future-proofing of cities in low-income and middle-income countries.
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在 COVID-19 大流行期间,全球城市交通模式发生了变化,包括步行、骑行、微出行和公共交通的使用,这为全球公共卫生提供了独特的经验和行动机会。我们旨在分析一项全球自然实验,探索大流行期间城市交通模式的变化,以及这些变化与 COVID-19 相关政策的实施之间的关系。
我们从苹果的移动趋势报告中获取了 296 个城市的城市交通指数数据,时间范围为 2020 年 1 月 13 日至 2022 年 2 月 4 日。交通指数代表了相对于大流行前(定义为 2020 年 1 月 13 日)的 100 基线值,苹果地图查询驾车、步行和公共交通出行的频率。根据国家收入水平、交通相关城市类型、人口密度和 COVID-19 大流行严重程度(SARS-CoV-2 感染率)对城市交通指数轨迹进行分层绘制。我们还使用 Shifting Streets 数据集综合了全球大流行政策和促进或限制城市交通和出行(步行、骑行和微出行、公共交通)的恢复行动。此外,一项全球范围内的自然实验评估了新的促进出行政策对全球各地城市步行和公共交通使用的影响。我们使用多变量回归和差分分析(DID),比较了干预前(2020 年 1 月 27 日至 4 月 12 日)和干预后(2020 年 4 月 13 日至 6 月 28 日)有和没有实施这些政策的城市,以探讨实施步行或公共交通促进政策是否会影响交通指数。
根据城市交通指数轨迹,我们观察到大流行开始时步行、驾车和公共交通的交通指数总体下降,但这些指数在 2020 年 4 月开始上升。人口密度较低的城市的驾车和步行指数通常高于人口密度较高的城市,而人口密度较高的城市的公共交通指数较高。大流行严重程度较高的城市的驾车和步行指数通常高于大流行严重程度较低的城市,而大流行严重程度较低的城市的公共交通指数高于其他城市。我们在数据集中确定了 587 项具有已知实施日期且与出行相关的政策,其中包括 305 项步行政策、321 项骑行和微出行政策以及 143 项公共交通政策,涉及 33 个国家的 230 个城市(19 个高收入国家、11 个中等收入国家和 3 个低收入国家)。在全球自然实验(包括 39 个城市)中,实施促进步行的政策干预措施与步行指数的绝对值显著增加相关(DID 系数 20.675 [95%CI 8.778-32.572]),而促进公共交通的政策干预措施则没有这种效果(0.600 [-13.293 至 14.494])。
我们的结果表明,为减轻 COVID-19 大流行而实施的政策在改变城市交通模式方面是有效的,特别是增加了出行。鉴于出行的已知益处,这些政策可以在大流行后得到维持、扩展和评估。不同收入国家之间干预措施的差异表明,改变基础设施以优先考虑安全步行、骑行和方便使用公共交通,可以帮助低收入和中等收入国家的城市未来更好地发展。
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