Spatio-temporal Adaptive Pricing for Balancing Mobility-on-Demand Networks

Abstract

Pricing in mobility-on-demand (MOD) networks, such as Uber, Lyft, and connected taxicabs, is done adaptively by leveraging the price responsiveness of drivers (supplies) and passengers (demands) to achieve such goals as maximizing drivers’ incomes, improving riders’ experience, and sustaining platform operation. Existing pricing policies only respond to short-term demand fluctuations without accurate trip forecast and spatial demand-supply balancing, thus mismatching drivers to riders and resulting in loss of profit. We propose CAPrice, a novel adaptive pricing scheme for urban MOD networks. It uses a new spatio-temporal deep capsule network (STCapsNet) that accurately predicts ride demands and driver supplies with vectorized neuron capsules while accounting for comprehensive spatio-temporal and external factors. Given accurate perception of zone-to-zone traffic flows in a city, CAPrice formulates a joint optimization problem by considering spatial equilibrium to balance the platform, providing drivers and riders/passengers with proactive pricing “signals.” We have conducted an extensive experimental evaluation upon over 4.0× 10 8 MOD trips (Uber, Didi Chuxing, and connected taxicabs) in New York City, Beijing, and Chengdu, validating the accuracy, effectiveness, and profitability (often 20% ride prediction accuracy and 30% profit improvements over the state-of-the-arts) of CAPrice in managing urban MOD networks.