# Inequity aversion reduces travel time in the traffic light control problem

## TL;DR

This paper introduces IACoLight, an improved traffic light control model that incorporates inequity aversion to enhance traffic flow, achieving up to 11.4% better performance than previous models by reshaping agent rewards.

## Contribution

It proposes a novel integration of inequity aversion into deep reinforcement learning for traffic control, exploring positive and negative reward adjustments for the first time.

## Key findings

- IACoLight outperforms CoLight by up to 11.4% in traffic flow efficiency.
- Rewarding advantageous inequities further improves traffic management.
- Incorporating inequity aversion reduces average vehicle travel time.

## Abstract

The traffic light control problem is to improve the traffic flow by coordinating between the traffic lights. Recently, a successful deep reinforcement learning model, CoLight, was developed to capture the influences of neighboring intersections by a graph attention network. We propose IACoLight that boosts up to 11.4% the performance of CoLight by incorporating the Inequity Aversion (IA) model that reshapes each agent's reward by adding or subtracting advantageous or disadvantageous reward inequities compared to other agents. Unlike in the other applications of IA, where both advantageous and disadvantageous inequities are punished by considering negative coefficients, we allowed them to be also rewarded and explored a range of both positive and negative coefficients. Our experiments demonstrated that making CoLight agents averse to inequities improved the vehicles' average travel time and rewarding rather than punishing advantageous inequities enhanced the results.