The Impact of Countdown Clocks on Subway Ridership in New York City
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Protecting the passengers' safety and increasing ridership are two never ending pursuits of public transit agencies. One of the proposed methods to achieve both goals for subway service is to implement real time train arriving countdown clocks in subway stations. Metropolitan Transportation Authority (MTA) of New York City (NYC) chose to install such countdown clocks in their stations starting from 2007 on a selection of subway lines. Due to the recent development of Bluetooth Beacon technology, the MTA could now install countdown clocks and train trackers in a non intrusive manner with much faster speed. As a result, the MTA is aiming to install countdown clocks in every subway station on every line. However, with such an aggressive plan, the impact of countdown clocks on subway ridership has not been fully studied. This paper proposes using Panel Regression methods, specifically, Random Effect (RE) model and Fixed Effect (FE) model to quantify the impact of countdown clocks on subway ridership. Machine Learning methods, namely Random Forest (RF) with AdaBoost and Decision Tree (DT) Regression, are also used as alternative data driven approaches for the FE and RE model. The results show that for the G line service, which runs between Brooklyn and Queens, the introduction of countdown clocks could increase weekly ridership by about 1783 per station. The study also found that the machine learning methods provide better accuracy in predicting the ridership than RE and FE models.
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