Strategic Planning for Flexible Agent Availability in Large Taxi Fleets

TL;DR

This paper introduces a scalable mechanism based on replicator dynamics and behavior cloning to optimize taxi driver schedules, improving individual revenue and fleet availability in large-scale, self-interested taxi systems.

Contribution

It presents a novel, scalable approach for optimizing agent availability in large taxi fleets, addressing driver constraints and improving system efficiency.

Findings

Significantly better policies than existing methods.

Improved individual agent revenue.

Enhanced overall agent availability.

Abstract

In large-scale multi-agent systems like taxi fleets, individual agents (taxi drivers) are self-interested (maximizing their own profits) and this can introduce inefficiencies in the system. One such inefficiency is with regard to the "required" availability of taxis at different time periods during the day. Since a taxi driver can work for a limited number of hours in a day (e.g., 8-10 hours in a city like Singapore), there is a need to optimize the specific hours, so as to maximize individual as well as social welfare. Technically, this corresponds to solving a large-scale multi-stage selfish routing game with transition uncertainty. Existing work in addressing this problem is either unable to handle ``driver" constraints (e.g., breaks during work hours) or not scalable. To that end, we provide a novel mechanism that builds on replicator dynamics through ideas from behavior cloning. We demonstrate that our methods provide significantly better policies than the existing approach in terms of improving individual agent revenue and overall agent availability.