The year 2020 is when everything changed due to the wide-scale effects of the COVID-19 Coronavirus Pandemic. How does Mass Transit cope without the capability of carrying masses?
Impacts of COVID-19 on Urban Rail Transit Ridership: Application of the Synthetic Control Method to 22 World Cities
Mengwei Xin (email@example.com), Harbin Institute of TechnologyShow Abstract
Amer Shalaby, University of Toronto
Shuming Feng, Harbin Institute of Technology
Hu Zhao, Harbin Institute of Technology
The outbreak of COVID-19 in 2020 has had drastic impacts on social economics and activities, including public transportation. This paper estimates the effect of COVID-19 on daily ridership change ratio of urban rail transit using the Synthetic Control Method. Six variables are selected as the predictors, among which four variables not affected by the pandemic are employed instead of the traditional variables generally used in ridership analysis. The other two variables, population and GDP per capita, are adopted to represent the individual effects across 22 cities around the world. Only the data before the pandemic for the two predictors are applied to exclude the effects of COVID-19 on the predictors. A total of 22 cities from China, Europe and the US with varying timelines of the pandemic outbreak are selected as the study cases. Half of these cities are considered as “treated units” and the remaining cities are used to generate the synthetic control unit for each treated unit. The effect of COVID-19 is estimated as the gap between the treated unit and the synthetic control unit. As an extension of the SCM application, the effect of the system closure in Wuhan on ridership recovery is analyzed. A series of placebo tests are rolled out to confirm the significance of the two analyses. About 90%reduction effect of COVID-19 on ridership is indicated in most Chinese cities. In addition, the negative effects of the system closure are confirmed since the recovery of Wuhan is slower than in other cities.
Non-Stationary Time Series Model for Station Based Subway Ridership During Covid-19 Pandemic (Case Study: New York City)
Bahman Moghimi, City College of New YorkShow Abstract
Camille Kamga, City College of New York
Abolfazl Safikhani, University of Florida
Sandeep Mudigonda, City College of New York
Patricio Vicuna, City College of New York
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 models 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 after 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.
Virus Transmission Risk in Urban Rail Systems: A Microscopic Simulation-based Analysis of Spatio-temporal Characteristics
Jiali Zhou, Northeastern UniversityShow Abstract
Haris Koutsopoulos, Northeastern University
Transmission risk of air-borne diseases in public transportation systems is a concern. The paper proposes a modified Wells-Riley model for risk analysis in public transportation systems to capture the passenger flow characteristics, including spatial and temporal patterns in terms of number of boarding, alighting passengers, and number of infectors. The model is utilized to assess overall risk as a function of OD flows, actual operations, and factors such as mask wearing, and ventilation. The model is integrated with a microscopic simulation model of subway operations (SimMETRO). Using actual data from a subway system, a case study explores the impact of different factors on transmission risk, including mask-wearing, ventilation rates, infectiousness levels of disease and carrier rates. In general, mask-wearing and ventilation are effective under various demand levels, infectiousness levels, and carrier rates. Mask-wearing is more effective in mitigating risks. Impacts from operations and service frequency are also evaluated, emphasizing the importance of maintaining reliable, frequent operations in lowering transmission risks. Risk spatial patterns are also explored, highlighting locations of higher risk.
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