Core-Selecting Auctions for Autonomous Vehicle Public Transportation System

Abstract

With the technological advancements, the autonomous vehicle (AV) is expected to play an active role in the future transportation system. An AV-based public transportation system was recently proposed to unleash the full capability of AVs to provide effective and flexible transportation services. It establishes a new public transportation market that can accommodate multiple vehicle operators. Such multi-tenant system encourages market competition for better quality of service. The pricing process in the original proposal was designed based on the Vickrey–Clarke–Groves mechanism, but it is vulnerable to the problems of payoff reduction and shill-bidding. In this paper, we reinvestigate the pricing process to address these vulnerabilities. We formulate it as core-selecting reverse combinatorial auctions and investigate the properties of the “core.” We establish a core-selecting mechanism that can maximize customer's utility and prevent shill-bidding. We analyze the theoretical properties of the formulated auctions and the core-selecting mechanism. We verify the results with extensive simulations. The simulation results show that the core-selecting mechanism can result in lower service charge, and suppress untruthfulness, shill-bidding, and coalition formation. It can produce auction results in linear computation time, making it scalable and practical.