Group Biometry, Biosignals, Security, and Smart Mobility, Departamento de Matemática Aplicada a las Tecnologías de la Información y las Comunicaciones, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain.
Sensors (Basel). 2021 Sep 30;21(19):6574. doi: 10.3390/s21196574.
COVID-19 has dramatically struck each section of our society: health, economy, employment, and mobility. This work presents a data-driven characterization of the impact of COVID-19 pandemic on public and private mobility in a mid-size city in Spain (Fuenlabrada). Our analysis used real data collected from the public transport smart card system and a Bluetooth traffic monitoring network, from February to September 2020, thus covering relevant phases of the pandemic. Our results show that, at the peak of the pandemic, public and private mobility dramatically decreased to 95% and 86% of their pre-COVID-19 values, after which the latter experienced a faster recovery. In addition, our analysis of daily patterns evidenced a clear change in the behavior of users towards mobility during the different phases of the pandemic. Based on these findings, we developed short-term predictors of future public transport demand to provide operators and mobility managers with accurate information to optimize their service and avoid crowded areas. Our prediction model achieved a high performance for pre- and post-state-of-alarm phases. Consequently, this work contributes to enlarging the knowledge about the impact of pandemic on mobility, providing a deep analysis about how it affected each transport mode in a mid-size city.
健康、经济、就业和流动性。本工作对西班牙中等城市（富恩拉夫拉达）的公共和私人流动性受新冠疫情影响进行了数据驱动的分析。我们的分析使用了 2020 年 2 月至 9 月期间从公共交通智能卡系统和蓝牙交通监测网络收集的真实数据，因此涵盖了疫情的相关阶段。结果表明，在疫情高峰期，公共和私人交通出行量分别骤降至新冠疫情前的 95%和 86%，此后后者恢复速度更快。此外，我们对日常模式的分析表明，用户在疫情不同阶段对流动性的行为发生了明显变化。基于这些发现，我们开发了短期公共交通需求预测模型，为运营商和交通管理人员提供准确的信息，以优化其服务并避免拥挤区域。我们的预测模型在警戒前和警戒后阶段都取得了很高的性能。因此，本工作有助于扩大对疫情对流动性影响的认识，并深入分析其对中等城市每种交通模式的影响。
