2019冠状病毒病大流行期间基于车站的地铁乘客量的非平稳时间序列模型（案例研究：纽约市）
Non-Stationary Time Series Model for Station Based Subway Ridership During Covid-19 Pandemic (Case Study: New York City)
摘要：
2020年的COVID-19疫情对交通系统造成了突然冲击，特别是纽约市的地铁乘客模式。通过统计模型了解地铁乘客量的时间模式在这种冲击中至关重要。然而，许多现有的统计框架可能不适合分析疫情期间的乘客量数据集，因为在此期间可能会违反一些建模假设。在本文中，利用变点检测程序，我们提出了一个分段平稳的时间序列模型来捕捉地铁客流量的非平稳结构。具体而言，该模型由几个独立的站为基础的自回归综合移动平均（ARIMA）模型连接在一起，在某些时间点。此外，数据驱动算法用于检测2019冠状病毒病大流行之前和期间的乘客模式变化以及估计模型参数。焦点的数据集是随机选择的纽约市地铁站的每日乘客量。拟合这些数据集的建议模型增强了我们对外部冲击期间乘客量变化的理解，无论是在均值（平均）变化还是时间相关性方面。
英文摘要：
The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City. Understanding the temporal pattern of subway ridership through statistical models is crucial during such shocks. However, many existing statistical frameworks may not be a good fit to analyze the ridership data sets during the pandemic since some of the modeling assumption might be violated during this time. In this paper, utilizing change point detection procedures, we propose a piece-wise stationary time series model to capture the nonstationary structure of subway ridership. Specifically, the model consists of several independent station based autoregressive integrated moving average (ARIMA) models concatenated together at certain time points. Further, data-driven algorithms are utilized to detect the changes of ridership patterns as well as to estimate the model parameters before and during the COVID-19 pandemic. The data sets of focus are daily ridership of subway stations in New York City for randomly selected stations. Fitting the proposed model to these data sets enhances our understanding of ridership changes during external shocks, both in terms of mean (average) changes as well as the temporal correlations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
