Effective intersection control can play an important role in reducing traffic congestion and associated vehicular emissions.

Effective intersection control can play an important role in reducing traffic congestion and associated vehicular emissions. This is vitally needed in developing countries, where air pollution is reaching life threatening levels. This paper presents EcoLight intersection control for developing regions, where budget is constrained and network connectivity is very poor. EcoLight learns effective control offline using state-of-the-art Deep Reinforcement Learning methods, but deploys highly efficient runtime control algorithms on low cost embedded devices that work stand-alone on road without server connectivity. EcoLight optimizes both average case and worst case values of throughput, travel time and other metrics, as evaluated on open-source datasets from New York and on a custom developing region dataset.

Effective intersection control can play an important role in reducing traffic congestion and associated vehicular emissions.

My main concern is that the paper, while being practical and useful in real-world, has limited technical contributions from an AI perspective. I do appreciate that the authors are trying to make the writing clear and simple, it somehow appears to be more like a collection of engineering efforts to make the system work. The paper is also written in a way that looks like a "technical report" rather than a machine learning type of paper. For example the Markov Decision Process underlying the problem is never formally defined, especially state transitions. It will be helpful to write down these definitions.

Effective intersection control can play an important role in reducing traffic congestion and associated vehicular emissions. This is vitally needed in developing countries, where air pollution is reaching life threatening levels. This paper presents EcoLight intersection control for developing regions, where budget is constrained and network connectivity is very poor. EcoLight learns effective control offline using state-of-the-art Deep Reinforcement Learning methods, but deploys highly efficient runtime control algorithms on low cost embedded devices that work stand-alone on road without server connectivity. EcoLight optimizes both average case and worst case values of throughput, travel time and other metrics, as evaluated on open-source datasets from New York and on a custom developing region dataset.