Recommended Citation
Postprint version. Published in Transportation Research Part C, Volume 14, Issue 4, August 1, 2006, pages 263-282.
The definitive version is available at https://doi.org/10.1016/j.trc.2006.08.002.
Abstract
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.
Disciplines
Electrical and Computer Engineering
Copyright
2006 Elsevier Ltd.
URL: https://digitalcommons.calpoly.edu/eeng_fac/103
