Marios Giouroukelis, Stella Papagianni, Nellie Tzivellou, Eleni I Vlahogianni, John C Golias
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.
