Human-Centric Traffic Signal Control for Equity: A Multi-Agent Action Branching Deep Reinforcement Learning Approach

TL;DR

This paper introduces MA2B-DDQN, a multi-agent deep reinforcement learning framework that explicitly optimizes traveler equity in traffic signal control, demonstrating improved fairness and scalability in complex urban scenarios.

Contribution

The paper presents a novel action-branching discrete control formulation and a human-centric reward, enabling scalable, equitable traffic signal coordination in multi-modal corridors.

Findings

Significantly reduces impacted travelers compared to baseline methods.

Demonstrates robustness with minimal variance across diverse scenarios.

Outperforms existing DRL approaches in multiple realistic traffic settings.

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 MA2B-DDQN, a human-centric multi-agent action-branching double Deep Q-Network (DQN) 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 coordination under discrete control while reducing the effective complexity of joint decision-making. We also design a human-centric reward that penalizes the number of delayed individuals in the corridor, accounting for pedestrians, vehicle occupants, and transit passengers. Extensive evaluations across seven realistic traffic scenarios in Melbourne, Australia, demonstrate that our approach significantly reduces the number of impacted travelers, outperforming existing DRL and baseline methods. Experiments confirm the robustness of our model, showing minimal variance across diverse settings. This framework not only advocates for a fairer traffic signal system but also provides a scalable solution adaptable to varied urban traffic conditions.