A Framework for the Joint Optimization of Assignment and Pricing in Mobility-on-Demand Systems with Shared Rides

Abstract

Mobility-on-Demand (MoD) systems have become a fixture in urban transportation networks, with the rapid growth of ride-hailing services such as Uber and Lyft. Ride-hailing is typically complemented with ridepooling options, which can reduce the negative externalities associated with ride-hailing services and increase the utilization of vehicles. Determining optimal policies for vehicle dispatching and pricing, two key components that enable MoD services, are challenging due to their massive scale and online nature. The challenge is amplified when the MoD platform offers exclusive (conventional ride-hailing) and shared services, and customers have the option to select between them. The pricing and dispatching problems are coupled because the realized demand depends on the quality of service (i.e., whom to share rides with) and the prices for each service type. We propose an integrated and computationally efficient method for solving the joint pricing and dispatching problem -- both when the problem is solved one request at a time or in batches (a common strategy in the industry). The main results of this research include showing that: (i) the sequential pricing problem has a closed-form solution under a multinomial logit (MNL) choice model, and (ii) the batched pricing problem is jointly concave in the expected demand distributions. To account for the spatial evolution of supply and demand, we introduce so-called retrospective costs to retain a tractable framework. Our numerical experiments demonstrate how this framework yields significant profit increases using taxicab data in Manhattan, New York City, compared to dynamic dispatching with static pricing policies.