Predicting real-time surge pricing of ride-sourcing companies

Abstract

Ride-sourcing companies such as Uber and Lyft represent a popular and growing mode of transit in cites worldwide. These companies employ surge pricing in real time to balance the needs of both drivers and riders. The prediction of surge prices in the next few minutes to hours encapsulates the complex evolution of service fleets and service demand in the short term. Surge pricing, if effectively predicted and disseminated to both drivers and riders, can be used to more efficiently allocate vehicles, save users money and time, and provide profitable insight to drivers, which ultimately helps the efficiency and reliability of transportation networks. This paper explores the spatio-temporal correlations between the urban environment, traffic flow characteristics, and surge multipliers. We propose a general framework for predicting the short-term evolution of surge multipliers in real-time using a log-linear model with L1 regularization, coupled with pattern clustering. This model is able to predict Uber surge multipliers in Pittsburgh up to two hours in advance using data from the previous hour out-performing the overall mean and the historical average in all but 3 of the 49 locations in Pittsburgh and outperforming three non-linear methods in 28 of the 49 locations. The model is able to out-perform the overall mean, historical mean, and non-linear methods on Lyft surge multipliers in Pittsburgh up to 20 min in advance. Cross-correlation of Uber and Lyft surge multipliers is also explored.