  Traffic congestion in urban road networks is a costly problem that affects
all major cities in developed countries. To tackle this problem, it is possible
(i) to act on the supply side, increasing the number of roads or lanes in a
network, (ii) to reduce the demand, restricting the access to urban areas at
specific hours or to specific vehicles, or (iii) to improve the efficiency of
the existing network, by means of a widespread use of so-called Intelligent
Transportation Systems (ITS). In line with the recent advances in smart
transportation management infrastructures, ITS has turned out to be a promising
field of application for artificial intelligence techniques. In particular,
multiagent systems seem to be the ideal candidates for the design and
implementation of ITS. In fact, drivers can be naturally modelled as autonomous
agents that interact with the transportation management infrastructure, thereby
generating a large-scale, open, agent-based system. To regulate such a system
and maintain a smooth and efficient flow of traffic, decentralised mechanisms
for the management of the transportation infrastructure are needed.
  In this article we propose a distributed, market-inspired, mechanism for the
management of a future urban road network, where intelligent autonomous
vehicles, operated by software agents on behalf of their human owners, interact
with the infrastructure in order to travel safely and efficiently through the
road network. Building on the reservation-based intersection control model
proposed by Dresner and Stone, we consider two different scenarios: one with a
single intersection and one with a network of intersections. In the former, we
analyse the performance of a novel policy based on combinatorial auctions for
the allocation of reservations. In the latter, we analyse the impact that a
traffic assignment strategy inspired by competitive markets has on the drivers
route choices. Finally we propose an adaptive management mechanism that
integrates the auction-based traffic control policy with the competitive
traffic assignment strategy.
