Ridesharing Price Prediction: Exploring the strategies of Dynamic Pricing

Abstract

This paper explores the application of dynamic pricing algorithms in rideshare industries and examines the key variables that influence trip prices by analyzing Uber and Lyft Dataset of Boston in 2018. This dataset contains Uber and Lyft data mainly from November and December 2018. It contains essential variables such like distance and price for rideshare price prediction, together with many other variables which could be included in regression model such as weather and hours in the day. The Ordinary Least Square regression method is used in this paper, and results shows that the most influential and statistically significant variables on price are the distance of trip and surge multiplier which indicates traffic surge. The regression result is in accordance with the intuition that price is positively correlated with distance and price goes up during surge time. The result implies that the dynamic pricing algorithm that adjusts live prices heavily relies on the current traffic condition and distance of the trip, while the price is not determined by hour of the trip during the day and temperature.