Human-centric traffic signal control for equity: A multi-agent action branching deep reinforcement learning approach

Abstract

Coordinating traffic signals along multimodal corridors is challenging because many multi-agent deep reinforcement learning (DRL) approaches remain vehicle-centric and struggle with high-dimensional discrete action spaces. We propose a Multi-Agent Action-Branching Double Deep Q-Network (MA2B-DDQN), a human-centric framework that explicitly optimizes traveler-level equity. Our key contribution is an action-branching discrete control formulation that decomposes corridor control into (i) local, per-intersection actions that allocate green time between the next two phases and (ii) a single global action that selects the total duration of those phases. This decomposition enables scalable coordina..