Corpus ID: 2073518

Traffic Lights with Auction-Based Controllers: Algorithms and Real-World Data

@article{Baluja2017TrafficLW,
  title={Traffic Lights with Auction-Based Controllers: Algorithms and Real-World Data},
  author={S. Baluja and M. Covell and R. Sukthankar},
  journal={ArXiv},
  year={2017},
  volume={abs/1702.01205}
}
  • S. Baluja, M. Covell, R. Sukthankar
  • Published 2017
  • Computer Science
  • ArXiv
  • Real-time optimization of traffic flow addresses important practical problems: reducing a driver's wasted time, improving city-wide efficiency, reducing gas emissions and improving air quality. Much of the current research in traffic-light optimization relies on extending the capabilities of traffic lights to either communicate with each other or communicate with vehicles. However, before such capabilities become ubiquitous, opportunities exist to improve traffic lights by being more responsive… CONTINUE READING
    Continuous Selection of Optimized Traffic Light Schedules: A Machine Learning Approach

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 52 REFERENCES
    Micro-Auction-Based Traffic-Light Control: Responsive, Local Decision Making
    19
    Approximating the Effects of Installed Traffic Lights: A Behaviorist Approach Based on Travel Tracks
    6
    SURTRAC: Scalable Urban Traffic Control
    28
    Auction-based autonomous intersection management
    135
    Schedule-Driven Coordination for Real-Time Traffic Network Control
    59
    Agent-Based Traffic Control Using Auctions
    58
    Reinforcement learning-based multi-agent system for network traffic signal control
    236