International Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443    ISSN (Online): 2394-7454 Volume-13 Issue-137 April-2026
  1. 4037
    Citations
  2. 2.7
    CiteScore
Hybrid FPA: a fairness-, pressure-, and emission-aware deep reinforcement learning framework for adaptive traffic signal control

Mihir R. Panchal1 and Pankaj P. Prajapati2

Research Scholar, Department of Electronics and Communication Engineering,Government Engineering College, Modasa, Gujarat Technological University, Ahmedabad, Gujarat,India1
Associate Professor, Department of Electronics and Communication Engineering,Vishwakarma Government Engineering College, Gujarat Technological University, Ahmedabad,Gujarat,India2
Corresponding Author : Mihir R. Panchal

Recieved : 22-August-2025; Revised : 19-April-2026; Accepted : 20-April-2026

Abstract

In urban networks, signalized intersections represent critical bottlenecks that often lead to fuel wastage, prolonged delays, and increased emissions of carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxides (NOx), hydrocarbons (HC), and particulate matter (PMx). Conventional fixed-time and actuated controllers tend to degrade under nonstationary demand, heterogeneous traffic composition, and recurrent traffic surges, resulting in congestion and environmental degradation. This study proposes a hybrid flow pressure-aware (Hybrid FPA) framework, a fairness–pressure–aware deep reinforcement learning (DRL) approach that integrates switching penalties, emission-weighted pressure, and fairness regularization into a unified reward function. The framework is supported by a double dueling deep Q-network (D3QN) with prioritized experience replay (PER) for enhanced stability, along with a compact one-dimensional convolutional (Conv1D) encoder. Using traffic data collected from unmanned aerial vehicles (UAVs), the proposed framework is evaluated on a Simulation of Urban MObility (SUMO)-based reconstruction of the Panjarapol Cross Road intersection in Ahmedabad. The performance is compared with state-of-the-art methods, including PressLight, convolutional block attention module D3QN (CBAM-D3QN), multi-directional D3QN (MD3QN), and partial detection D3QN (PD3QN). Experimental results over 100 training episodes demonstrate that Hybrid FPA reduces average waiting time by approximately 78% and queue length by about 65%, while also decreasing fuel consumption by nearly 27% and CO₂ emissions by 29%. Additionally, PMx emissions are reduced by nearly 20%, HC by over 20%, and NOx by approximately 25%, along with reductions in other pollutants. The phase-switching frequency is also lowered by more than one-third, contributing to smoother traffic flow and further indirect emission reductions. These findings indicate that reinforcement learning can effectively achieve both mobility and sustainability objectives by explicitly incorporating fairness, pressure balancing, and multi-pollutant optimization. Therefore, Hybrid FPA represents a dynamic and efficient paradigm for adaptive traffic signal control with strong potential for real-world implementation.

Keywords

Hybrid flow pressure-aware (Hybrid FPA), Deep reinforcement learning (DRL), Traffic signal control, Emission reduction, Intelligent transportation systems, SUMO simulation.

Cite this article

Panchal MR, Prajapati PP. Hybrid FPA: a fairness-, pressure-, and emission-aware deep reinforcement learning framework for adaptive traffic signal control. International Journal of Advanced Technology and Engineering Exploration. 2026;13(137):541-565. DOI : 10.19101/IJATEE.2025.121221179

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