Exploring the dynamics of surge pricing in mobility-on-demand taxi services

Dynamic pricing implemented in the form of a surge price multiplier (SPM) by mobility-on-demand services such as Uber, Lyft, etc. have significantly altered the demand-supply dynamics of the fixed fare rate traditional taxi market. However, it bears a fair share of criticism for being opaque, opportunistic, and socially insensitive, especially during large public events and emergency situations. In this paper, we collect and mine the operational data of one of the largest mobility service provider: Uber in the New York City (NYC) to understand the underlying mechanism behind the dynamic pricing generation. We find the common spatiotemporal patterns in the SPM and identify the cost-effectiveness of its most popular service, UberX as compared to UberBlack and street hailing taxis. We model the underlying phenomenon behind the SPM generation as a function of demand, supply, the time of the day, the day of the week, and expected time to arrival (ETA) using various machine learning classifiers. Support vector machines, k-nearest neighbor, and decision tree classifiers are found to model the SPM the best with the average classification loss for the 10-fold cross validation being as low as 0.001 for the rapidly changing SPM for UberX.