Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions.

ABSTRACT: Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human 'player' to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference lear

SUBMITTER: Box S 

PROVIDER: S-EPMC4448775 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature