Market-based control of computational systems: introduction to the special issue

Gerding, Enrico H.; McBurney, Peter; Yao, Xin

doi:10.1007/s10458-009-9114-9

Cited by 2 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, we can see the traffic assignment problem as a distributed choice and allocation problem, since a set of resources (i.e., the links capacity) must be allocated to a set of agents (i.e., the drivers). To this regard, markets as mediators for distributed resource allocation problems have been applied to several socio-technical systems (Gerding et al, 2010).…”

Section: Competitive Traffic Assignment (Cta)mentioning

confidence: 99%

“…To extend Dresner and Stone's approach to a network of intersections, we focus on the problem of traffic assignment, conceived as a distributed choice problem where intersection managers try to affect the decision making of the driver agents. In particular, we use markets as mediators for our distributed choice and allocation problem (Gerding, McBurney, & Yao, 2010). Our contribution to the attainment of the above objective is twofold.…”

Section: Single Intersectionmentioning

confidence: 99%

“…In Section 5.1 we evaluate how market-inspired methods (Gerding et al, 2010) can be applied as traffic assignment strategies for networks of reservation-based intersections. The idea is that, if there is a market where drivers acquire the necessary reservations to pass through the intersections of the urban network, this market, and the intersection managers that operate in it from the supply side, can be designed to work as a traffic assignment system.…”

Section: Network Of Intersectionsmentioning

confidence: 99%

See 2 more Smart Citations

A Market-Inspired Approach for Intersection Management in Urban Road Traffic Networks

Vasirani¹,

Ossowskí²

2012

jair

140

View full text Add to dashboard Cite

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 reservationbased 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.

show abstract

Section: Competitive Traffic Assignment (Cta)mentioning

confidence: 99%

Section: Single Intersectionmentioning

confidence: 99%

Section: Network Of Intersectionsmentioning

confidence: 99%

See 1 more Smart Citation