Distributed Learning Control of Traffic Signals

Authors:
Y. J. Cao;N. Ireson;Larry Bull;R. Miles
Affiliations:
-;-;-;-
Venue:
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Year:
2000

Citing 3
Cited 2

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Zcs: A zeroth level classifier system

Evolutionary Computation
Evolving control laws for a network of traffic signals

GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation

A Bigger Learning Classifier Systems Bibliography

IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Organic Control of Traffic Lights

ATC '08 Proceedings of the 5th international conference on Autonomic and Trusted Computing

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Abstract

This paper presents a distributed learning control strategy for traffic signals. The strategy uses a fully distributed architecture in which there is effectively only one (low) level of control. Such strategy is aimed at incorporating computational intelligence techniques into the control system to increase the response time of the controller. The idea is implemented by employing learning classifier systems and TCP/IP based communication server, which supports the communication service in the control system. Simulation results in a simplified traffic network show that the control strategy can determine useful control rules within the dynamic traffic environment, and thus improve the traffic conditions.