Giouroukelis Marios, Papagianni Stella, Tzivellou Nellie, Vlahogianni Eleni I, Golias John C
Department of Transportation Planning and Engineering, School of Civil Engineering, National Technical University of Athens, 5 Iroon Polytechniou str, Athens 15 773, Greece.
Transport for Athens - OASA S.A., 15, Metsovou str, Athens 106 82, Greece.
Case Stud Transp Policy. 2022 Jun;10(2):1069-1077. doi: 10.1016/j.cstp.2022.03.023. Epub 2022 Mar 30.
Short-term demand forecasting is essential for the public transit system, allowing for effective operations planning. This is especially relevant in the highly uncertain environment created by the SARS‑CoV‑2 pandemic. In this paper, we attempt to develop accurate prediction models of transit ridership in Athens, Greece, using Autoregressive Fractional Integrated time series models enhanced with SARS‑CoV‑2-related exogenous variables. The selected exogenous variables are, from the one hand, the ratio of weekly SARS‑CoV‑2 infections over the infections 3 weeks before (capturing the dynamics of the pandemic, as a proxy for fear of transmitting the disease while commuting), and from the other hand, an index of the stringency of the government's SARS‑CoV‑2-related measures and regulations. The developed ARFIMAX models have been fitted separately on bus and metro ridership data and wield comparable and statistically significant results. In both models, the exogenous variables prove to be statistically significant and their values are intuitive, suggesting a linear interrelation between them and transit ridership.
短期需求预测对于公共交通系统至关重要，有助于进行有效的运营规划。这在严重急性呼吸综合征冠状病毒2（SARS-CoV-2）大流行所造成的高度不确定环境中尤为重要。在本文中，我们尝试利用增强了与SARS-CoV-2相关外生变量的自回归分数整合时间序列模型，来建立希腊雅典公交客流量的准确预测模型。所选的外生变量一方面是每周SARS-CoV-2感染病例数与前三周感染病例数的比率（反映大流行的动态，作为通勤时对传播疾病的恐惧的代理指标），另一方面是政府与SARS-CoV-2相关的措施和法规的严格程度指数。所开发的自回归分数整合移动平均模型（ARFIMAX）已分别应用于公交和地铁客流量数据，并取得了可比且具有统计学意义的结果。在这两个模型中，外生变量均被证明具有统计学意义，其数值直观，表明它们与公交客流量之间存在线性相互关系。
