Stochastic Flow Models with Delays and Applications to Multi-Intersection Traffic Light Control
For traffic engineers, this provides a method to optimize traffic light cycles with delays, improving congestion management.
The paper extends Stochastic Flow Models to include delays and applies them to multi-intersection traffic light control, deriving gradient estimates for congestion costs. The inclusion of delays improves performance compared to models that ignore delays.
We extend Stochastic Flow Models (SFMs), used for a large class of discrete event and hybrid systems, by including the delays which typically arise in flow movement. We apply this framework to the multi-intersection traffic light control problem by including transit delays for vehicles moving from one intersection to the next. Using Infinitesimal Perturbation Analysis (IPA) for this SFM with delays, we derive new on-line gradient estimates of several congestion cost metrics with respect to the controllable green and red cycle lengths. The IPA estimators are used to iteratively adjust light cycle lengths to improve performance and, in conjunction with a standard gradient-based algorithm, to obtain optimal values which adapt to changing traffic conditions. We introduce two new cost metrics to better capture congestion and show that the inclusion of delays in our analysis leads to improved performance relative to models that ignore delays.