In the vibrant landscape of New York City, the yellow taxi industry stands as an iconic symbol and alifeline for countless city residents and tourists. The availability and effective distribution of yellowtaxis throughout the day play a pivotal role in meeting the ever-evolving demands of urbantransportation.
In our project, we focus on analyzing the time series demand of New York yellow taxis on an hourlyand monthly basis. We will experiment with classical decomposition, smoothing techniques, and(S)ARIMA models to select the most robust forecasting model to accurately predict ride demand.Our aim is to uncover the underlying trends, seasonality, and patterns, especially during peak hours,to gain valuable insights into the city's transportation dynamics. By doing so, we strive to makerecommendations to not only enhance the operational efficiency of taxi services but also contributeto a more efficient and responsive urban transportation system in New York City. Taxi demandforecasting offers real-time applications with significant benefits and impacts on the transportationecosystem. It enables taxi service providers to allocate resources efficiently, implement dynamicpricing, and optimize driver schedules. It aids traffic management, reduces empty cruising, andpromotes environmental sustainability. For passengers, it reduces waiting times and improvesconvenience.
Our project focuses on several key questions related to taxi service and passenger behavior. We aimto determine the peak daily demand for taxis and whether it varies day-to-day. We will identify thebusiest month and track demand trends within it. We'll pinpoint the most revenue-generating ridetypes and predict when longer rides are booked. Lastly, we'll explore common destinations for ourtaxi service users. These questions are the basis for our research and analysis.The dataset originates from the official website of the City of New York and has been provided byauthorized technology providers participating in the Taxicab & Livery Passenger EnhancementPrograms for the NYC Taxi and Limousine Commission (TLC).Our primary variables include total revenue, time, number of rides, distance, location, passengercount etc. KPIs, we look to address include Number of rides in an hour/day, Revenue/ride,Revenue/min, and Revenue/mile.
The NYC taxi time series analysis offers vital insights for strategic decision-making. This includesidentifying peak hours for potential surge pricing, which informs resource allocation and pricingstrategies. Demand forecasting ensures efficient driver and vehicle deployment, upholding servicequality. Additionally, operational efficiency can be improved by identifying and addressinginefficiencies through responsive measures.