Modeling Uber Data for Predicting Features Responsible for Price Fluctuations

In the field of economics, the features and patterns of the transportation system, including classical modes of transportation such as subways and taxis, as well as innovative tools such as car pooling platforms(Uber, Lyft, etc), are key research topics. The study here demonstrates how an Uber dataset is, which comprises Uber's New York City data, works. Uber is an online service provider platform via internet or a mobile application that avails ride-hailing service. In essence, it matches passengers with drivers of vehicles to book a ride from one place to another. The service connects users with drivers who will drive them to their desired location. The dataset contains primary data about Uber pick-ups, including the date, time, longitude, and latitude coordinates. The paper attempts to examine data from different locations, weathers, hours, and dates (intraday and midweek) in New York City and apply time series data analysis, statistical regression on the dataset, and predict Uber ride prices. We arrive at conclusions by analyzing data using various graphs, calculating and estimating the influence of these elements on Uber riders’ payment amounts, and emphasizing features that cause price fluctuation.