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Bayesian Multilevel Models for Ridership Demand using Rainfall

The Northeast United States, particularly New York State has experienced an increase in extreme 24-hour precipitation during the past 50 years (Horton et al., 2011). Recent events such as Hurricane Irene and Superstorm Sandy have revealed vulnerability to intense precipitation within the transportation sector. Stronger knowledge of extreme events and the resultant simultaneous regional network vulnerabilities can support emergency management division in creating more effective response systems. There is a necessity to understand the underlying reasons that generate the spatial-temporal demand. There is also a necessity to understand and forecast, based on climate, individual level behavior and their nodal functions during a simultaneous extreme rainfall event.

Project Details

Author(s): 
Dr. Naresh Devineni
Dr. Mahdieh Allahviranloo
Universities: 
City University of New York
Publication Year: 
2017
Publication Type: 
Final Report
Project: 
An Agent-­Based Disaster Response Inference Model for Assessment of Transportation Risk under Extreme Events
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