Abstract
Amid significant increases in ridership (9.8% over the past 5 years) on the more than 100 year-old New York City Transit (NYCT) subway system, NYCT has become aware of increased crowding on station platforms. Because of limited platform capacity, platforms become crowded even during minor service disruptions. A real-time model was developed to estimate crowding conditions and to predict crowding for 15 min into the future. The algorithm combined historical automated fare collection data on passenger entry used to forecast station entrance, automated fare collection origin–destination inference information used to assign incoming passengers to a particular direction and line by time of day, and general transit feed specification–real time data to determine predicted train arrival times used to assign passengers on the platform to an incoming train. This model was piloted at the Wall Street Station on the No. 2 and No. 3 Lines in New York City’s Financial District, which serves an average 28,000 weekday riders, and validated with extensive field checks. A dashboard was developed to display this information graphically and visually in real time. On the basis of predictions of gaps in service and, consequently, high levels of crowding, dispatchers at NYCT’s Rail Control Center can alter service by holding a train or skipping several stops to alleviate any crowding conditions and provide safe and reliable service in these situations.
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