Postprint version. Published in Transportation Research Part C, Volume 14, Issue 4, August 1, 2006, pages 263-282. Copyright © 2006 Elsevier Ltd. The definitive version is available online at: http://dx.doi.org/10.1016/j.trc.2006.08.002.
An adaptive control model of a network of signalized intersections is proposed based on a discrete-time, stationary, Markov decision process. The model incorporates probabilistic forecasts of individual vehicle actuations at downstream inductance loop detectors that are derived from a macroscopic link transfer function. The model is tested both on a typical isolated traffic intersection and a simple network comprised of five four-legged signalized intersections, and compared to full-actuated control. Analyses of simulation results using this approach show significant improvement over traditional full-actuated control, especially for the case of high volume, but not saturated, traffic demand.
Electrical and Computer Engineering