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A study on variability of cost difference between Green and Yellow NYC taxi rides over time
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This thesis examines the interplay across diverse mobility options in New York City, focusing on
cost variability and commuter behavior. This work investigates the evolution of fare disparities
between taxis, using comprehensive datasets from NYC’s Taxi and Limousine Commission, along
with advanced predictive modeling approaches such as Long Short-Term Memory and Gradient
Boosting. The study revealed how commuter choices are influenced by operational regions and
pricing models: key findings highlight significant temporal and spatial fare trends. Enhanced taxi
coverage in NYC’s outer boroughs has led to a measurable shift in subway ridership. Green Taxis,
designed to address transportation gaps in underserved neighborhoods, exhibit limited success,
particularly in low-income areas, where reliance on private vehicles remains dominant.
Additionally, a comparative analysis of dynamic versus fixed pricing models showcased the
flexibility of ride-hailing platforms like Uber in addressing peak demands and spatial variations,
differing from the rigidity observed in traditional pricing systems. These analyses provide critical
insights into spatiotemporal variations in taxi demand, including the differential impacts of public
transit availability and demographic factors.
By addressing pricing disparities and enhancing service accessibility, this work concludes by
presenting actionable recommendations to improve equity and efficiency within NYC’s
transportation network. The findings aim to guide policymakers in developing adaptive and
sustainable mobility strategies tailored to the evolving urban landscape.