Deep Deterministic Policy Gradient for Urban Traffic Light Control

TL;DR

This paper applies Deep Deterministic Policy Gradient (DDPG) to optimize urban traffic light timing, addressing large-scale input challenges and demonstrating effectiveness across simple to complex traffic network scenarios.

Contribution

It introduces a DDPG-based approach for urban traffic light control, enabling handling of large-scale traffic data beyond localized intersection models.

Findings

Effective in small intersection scenarios

Scalable to large city sections

Outperforms traditional methods in simulations

Abstract

Traffic light timing optimization is still an active line of research despite the wealth of scientific literature on the topic, and the problem remains unsolved for any non-toy scenario. One of the key issues with traffic light optimization is the large scale of the input information that is available for the controlling agent, namely all the traffic data that is continually sampled by the traffic detectors that cover the urban network. This issue has in the past forced researchers to focus on agents that work on localized parts of the traffic network, typically on individual intersections, and to coordinate every individual agent in a multi-agent setup. In order to overcome the large scale of the available state information, we propose to rely on the ability of deep Learning approaches to handle large input spaces, in the form of Deep Deterministic Policy Gradient (DDPG) algorithm. We performed several experiments with a range of models, from the very simple one (one intersection) to the more complex one (a big city section).