A Multiagent System for Optimizing Urban Traffic
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
A Distributed Approach for Coordination of Traffic Signal Agents
Autonomous Agents and Multi-Agent Systems
Using cooperative mediation to coordinate traffic lights: a case study
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Multiagent traffic management: an improved intersection control mechanism
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
MASON: A Multiagent Simulation Environment
Simulation
Evolving control laws for a network of traffic signals
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
A market-inspired approach to reservation-based urban road traffic management
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Designing Intelligent Agents For Traffic Delay Compensation
Journal of Integrated Design & Process Science
A multiagent approach to autonomous intersection management
Journal of Artificial Intelligence Research
Learning in groups of traffic signals
Engineering Applications of Artificial Intelligence
Long-term fairness with bounded worst-case losses
Autonomous Agents and Multi-Agent Systems
Evolving individual behavior in a multi-agent traffic simulator
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Distributed and adaptive traffic signal control within a realistic traffic simulation
Engineering Applications of Artificial Intelligence
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What if traffic lights gave you a break after you've spent a long time waiting in traffic elsewhere? In this paper we examine a variety of multi-agent traffic light controllers which consider vehicles' past stopped-at-red histories. For example, a controller might distribute credits to cars as they wait and award the green light to lanes with the most credits, allowing cars to keep the credits they accumulate during travel. Such history-based controllers are intended to provide a kind of global fairness, reducing the variance in mean time spent waiting at lights during trips. We compare these controllers against other multi-agent controllers which only consider present information, and discover, among other things, that while the history-based controllers are among the most robust, they often unexpectedly provide more efficiency than fairness.