Impact of Weather on Transit Revenue in New York City

Abstract

A goal of transit revenue/ridership analysis is to identify both long-term and short-term trends that explain variances from budget and changes over time. One of the variables that impacts revenue/ridership is weather. Since weather can mask underlying trends, it is useful to quantify and remove the impact of weather from the revenue/ridership data. In this paper, we use an Ordinary Least Square (OLS) model to determine the impact of temperature, rain, and snow on MTA New York City Transit’s daily revenue/ridership. We find that the magnitude of the weather impact differs by mode of transportation (subway, bus), day of week (weekday, weekend), and season (winter, summer, spring/fall). We also provide an application of the model to our data. Our results indicate that the model does a good job in explaining the impact of weather on our revenue/ridership.

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