Contributers - Chun-Chieh Tsai, Isha Chaturvedi, Rachel Lim, Ben Steers Yi Xuan Tang
Travel behavior are affected my multiple factors; weather is commonly identified as a key factor influencing the way people commute in the city. While existing studies have focused on the seasonality of weather on travel behaviour, our study aims to fill the gap in research by investigating the effect of variations in weather conditions on a more granular temporal scale by comparing intra-day weather conditions with subway commuter volumes across all subways stations in New York City. Using entry counts from the MTA subway turnstile dataset and weather data, regression models are applied to analyze the effect of weather on commuter volumes. Results from time series analysis reveal that there is a clear periodicity in commuter volumes with distinct clustering of entries. Results from modelling reveal that while all features are important, the predictive power of commuter volume solely on weather conditions is insufficient, as travel behaviour is influenced by a complex array of factors. Nevertheless, this study contributes to existing understanding of travel behaviour on public transit through a more intricate analysis of ridership volume at a finer temporal and spatial scale.