Fair Planning for Mobility-on-Demand with Temporal Logic Requests

Abstract: Mobility-on-demand systems are transforming the way we think about the transportation of people and goods. Most research effort has been placed on scalability issues for systems with a large number of agents and simple pick-up/drop-off demands. In this paper, we consider fair multi-vehicle route planning with streams of complex, temporal logic transportation demands. We consider an approximately envy-free fair allocation of demands to limited-capacity vehicles based on agents' accumulated utility over a finite time horizon, representing for example monetary reward or utilization level. We propose a scalable approach based on the construction of assignment graphs that relate agents to routes and demands, and pose the problem as an Integer Linear Program (ILP). Routes for assignments are computed using automata-based methods for each vehicle and demands sets of size at most the capacity of the vehicle while taking into account their pick-up wait time and delay tolerances. In addition, we integrate utility-based weights in the assignment graph and ILP to ensure approximative fair allocation. We demonstrate the computational and operational performance of our methods in ride-sharing case studies over a large environment in mid-Manhattan and Linear Temporal Logic demands with stochastic arrival times. We show that our method significantly decreases the utility deviation between agents and the vacancy rate.