Forecasting NYC Yellow Taxi Ridership Decline: A Time Series Analysis of Daily Passenger Counts (2017-2019)

TL;DR

This paper analyzes and forecasts the declining trend in NYC yellow taxi ridership from 2017 to 2019 using time series models, identifying seasonal patterns and the most accurate predictive approach.

Contribution

It introduces an effective time series modeling framework, particularly ARIMA, for predicting taxi ridership decline, with detailed analysis of seasonal effects and model performance.

Findings

Strong seasonal patterns identified in ridership data

A first-order autoregressive model achieved the best prediction accuracy

Ridership declined by approximately 200 passengers daily over the period

Abstract

This study analyzes and forecasts daily passenger counts for New York City's iconic yellow taxis during 2017-2019, a period of significant decline in ridership. Using a comprehensive dataset from the NYC Taxi and Limousine Commission, we employ various time series modeling approaches, including ARIMA models, to predict daily passenger volumes. Our analysis reveals strong seasonal patterns, with a consistent linear decline of approximately 200 passengers per day throughout the study period. After comparing multiple modeling approaches, we find that a first-order autoregressive model, combined with careful detrending and cycle removal, provides the most accurate predictions, achieving a test RMSE of 34,880 passengers on a mean ridership of 438,000 daily passengers. The research provides valuable insights for policymakers and stakeholders in understanding and potentially addressing the declining trajectory of NYC's yellow taxi service.