For his independent research topic, Xiaoqiang (Jason) Chen examined the relationship between the built environment and time-of-day ridership patterns at subway stations in New York City. He analyzed how these daily activity patterns vary from station to station, and the relationship between these patterns and land uses surrounding the stations. He then developed a method to forecast the time-of-day ridership patterns for subway stations in New York City. His advisors were Dr. Cynthia Chen of The City College of New York, and James Barry of MTA New York City Transit.

Jason’s research examined how subway ridership evolved over a 24 hour day, developed a methodology to classify the station’s time-of-day ridership pattern and to forecast the time-of- day ridership patterns for subway stations in New York City. He focused on several key research questions, including:

- How does ridership distribution differ over a 24 hour period for different stations and can any patterns be identified?

- What is the connection between the timeof- day ridership pattern and local features such as population, employment, and land use, and what are some network position effects such as general travel cost to Central Business District (CBD)?

- How can time-of-day ridership patterns for a particular station be reliably predicted?

The project also investigated the spatial distribution of subway station ridership in New York City on weekdays and weekends.