Randomized routing strategies of fleets of CAVs may prove market efficient
Multiagent Systems
Summary
The authors study future city transportation where cars can be human-driven or autonomously controlled by fleets managed by competing companies. They find that when people have different opinions about self-driving cars, making autonomous cars follow random routes can actually improve overall efficiency. However, this randomness can hurt regular drivers, so the authors suggest changing the market goals to balance company profits with overall travel time for everyone. This approach encourages companies to cooperate in ways that benefit the whole city rather than just themselves.
Authors
Grzegorz Jamróz, Łukasz Gorczyca, Rafał Kucharski
Abstract
In future cities every driver may own a vehicle which could be either independently driven (HDV), or autonomously routed and piloted (CAV). The autonomous operations could be handled by a few competing companies. What is the market structure which would make this market aligned with city goals? In this paper we discuss a variant of the emerging market of collectively routed fleets of CAVs, where revenue for fleet operators is proportional to market share. We provide benchmark scenarios to compare the routing algorithms. We present several routing algorithms and demonstrate that, when the attitudes of human drivers towards CAVs exhibit significant diversity, randomised CAV routing, resulting in unpredictable travel times for HDVs, is more efficient than routing proportional to system optimum/user equilibrium. Based on this, we propose to improve the design of the market by augmenting the market-share objective with mean systemwide travel time in order to limit antisocial randomised strategies of fleet operators and drive the competition towards social welfare oriented cooperation.