Does demand for subway ridership in Manhattan depend on the rainfall events?. (February 2019)

• Record Type: Journal Article
• Authors: Najafabadi, Shirin; Hamidi, Ali; Allahviranloo, Mahdieh; Devineni, Naresh
• Is Part Of: Transport policy. Volume 74(2019)
• Page Start: 201
• Page End: 213
• Publication Date: 2019-02
• Subjects: Bayesian multi-level regression model -- Subway ridership -- MCMC sampling -- Radar rainfall; Transportation and state -- Periodicals; Transportation -- Rates -- Periodicals; 388
• DOI: 10.1016/j.tranpol.2018.11.019
• Languages: English
• ISSNs: 0967-070X

Abstract

Abstract: The Northeast United States, particularly New York State has experienced an increase in extreme daily precipitation during the past 50 years. Recent events such as Hurricane Irene and Superstorm Sandy, have revealed vulnerability to the intense precipitation within the transportation sector. In the scale of New York City, where transit system is the most dominant mode of transportation and daily mobility of millions of passengers depends on it, any disruption in the transit service would result in gridlocks and massive delays. To assess the impacts of rainfall on the subway ridership, we merged high resolution radar rainfall and subway ridership data to conduct a detailed analysis for each of the 116 subway stations at the borough of Manhattan. The analysis is carried out on both hourly and daily resolution level, where a spatial-temporal Bayesian multi-level regression model is used to capture the underlying dependency between the parameters. The estimation results are obtained through Markov Chain Monte Carlo sampling method. The results for daily analysis indicate that during weekdays, transit ridership in the stations located in commercial zones are less sensitive to the rainfall compared to the ones in residential zones. Highlights: Impacts of spatial variability of rainfall on subway ridership is assessed using large scale ridership and rainfall data. A unified multi scale model, combining time and spatial scales for ridership is proposed. The study framework can be used by agencies to improve system efficiency and resilience at the station level.