@article{ART003353376},
author={Sunghyuck Hong},
title={AIoT-Driven Intelligent Traffic Signal Control with Smart Poles-A Simulation-Based Approach Using VEINS},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2026},
volume={12},
number={3},
pages={15}
TY - JOUR
AU - Sunghyuck Hong
TI - AIoT-Driven Intelligent Traffic Signal Control with Smart Poles-A Simulation-Based Approach Using VEINS
JO - Journal of Internet of Things and Convergence
PY - 2026
VL - 12
IS - 3
PB - The Korea Internet of Things Society
SP - 15
EP -
SN - 2466-0078
AB - Urban traffic congestion causes travel delay, fuel consumption, emissions, and productivity loss. Conventional fixed-time and actuated traffic signal controllers have limitations in responding to dynamic traffic demand and infrastructure-based sensing information. This paper proposes an AIoT-driven intelligent traffic signal control system that integrates smart poles and a Deep Q-Network (DQN)-based reinforcement learning controller. The proposed system collects traffic density, queue length, average delay, current signal phase, and V2I communication messages through smart poles equipped with cameras, loop detectors, communication modules, and edge computing devices. The DQN agent determines signal phases in real time based on the observed traffic state. The system is evaluated using VEINS, which integrates SUMO and OMNeT++ for joint traffic and communication simulation. Thirty independent simulation runs are conducted under identical traffic demand scenarios with different random seeds, and the statistical significance of the results is evaluated using ANOVA and post-hoc tests. The results show that the proposed AIoT-DQN controller improves average waiting time, queue length, throughput, and emission-related indicators compared with fixed-time and actuated control methods. The study demonstrates that smart pole-based AIoT infrastructure can enhance the real-time adaptability, reproducibility, and communication-aware decision-making capability of reinforcement learning-based traffic signal control.
KW - Artificial Intelligence of Things;Smart Poles;Intelligent Traffic Signal Control;Reinforcement Learning;VEINS Simulation
DO -
UR -
ER -
Sunghyuck Hong. (2026). AIoT-Driven Intelligent Traffic Signal Control with Smart Poles-A Simulation-Based Approach Using VEINS. Journal of Internet of Things and Convergence, 12(3), 15.
Sunghyuck Hong. 2026, "AIoT-Driven Intelligent Traffic Signal Control with Smart Poles-A Simulation-Based Approach Using VEINS", Journal of Internet of Things and Convergence, vol.12, no.3 15.
Sunghyuck Hong "AIoT-Driven Intelligent Traffic Signal Control with Smart Poles-A Simulation-Based Approach Using VEINS" Journal of Internet of Things and Convergence 12.3 15 (2026) : 15.
Sunghyuck Hong. AIoT-Driven Intelligent Traffic Signal Control with Smart Poles-A Simulation-Based Approach Using VEINS. 2026; 12(3), 15.
Sunghyuck Hong. "AIoT-Driven Intelligent Traffic Signal Control with Smart Poles-A Simulation-Based Approach Using VEINS" Journal of Internet of Things and Convergence 12, no.3 (2026) : 15.
Sunghyuck Hong. AIoT-Driven Intelligent Traffic Signal Control with Smart Poles-A Simulation-Based Approach Using VEINS. Journal of Internet of Things and Convergence, 12(3), 15.
Sunghyuck Hong. AIoT-Driven Intelligent Traffic Signal Control with Smart Poles-A Simulation-Based Approach Using VEINS. Journal of Internet of Things and Convergence. 2026; 12(3) 15.
Sunghyuck Hong. AIoT-Driven Intelligent Traffic Signal Control with Smart Poles-A Simulation-Based Approach Using VEINS. 2026; 12(3), 15.
Sunghyuck Hong. "AIoT-Driven Intelligent Traffic Signal Control with Smart Poles-A Simulation-Based Approach Using VEINS" Journal of Internet of Things and Convergence 12, no.3 (2026) : 15.
