This study investigates how adaptable Machine Learning Traffic Signal control methods are to topological variability. We ask how well can these methods generalize to non-Manhattan-like networks with non-uniform distances between intersections. A Machine Learning method that is highly reliable in various topologies is proposed and compared with state-of-the-art alternatives. Lastly, we analyze the sustainability of different traffic signal control methods based on computational efforts required to achieve convergence and perform training and testing. We show that our method achieves an approximately seven-fold improvement in terms of CO$$_2$$
2
emitted in training over the second-best method.
This study investigates various methods for autonomous traffic signal control. We look into different types of control methods, including fixed time, adaptive, analytic, and reinforcement learning approaches. Machine learning approaches are compared with the "analytic" approach, which is used as "gold standard" for performance assessment. We find that conventional machine learning approaches are better than the analytic approach, but require a lot more computer power. We, therefore, introduce a novel hybrid method called "analytically guided reinforcement learning" or shorter "α-RL". This approach is implemented in our "GuidedLight agent" and tends to outperform both, classical machine learning and the analytic approach, while largely improving convergence. This method is therefore suited as a "green IT" solution that improves environmental impact in a two-fold way: by reducing (i) traffic congestion and (ii) the processing power needed for the learning and operation of the traffic light control algorithm.
Data-driven and machine-learning-based methods are increasingly used in attempts to master the challenges of the world. But are they really the best approaches to manage complex dynamical systems? Our aim is to gain more insights into this question by studying various popular reinforcement learning methods for traffic signal control, namely in disrupted scenarios characterized by significant, unpredictable variations. The results are expected to be relevant in subject areas ranging from traffic physics to transportation theory, from dynamics in networks to complex systems, from control theory to self-organization, and from adaptive heuristics to machine learning.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.