Gap-Fuzzy Adaptive Signal Control: Enhancing Urban Traffic Efficiency

Juan Pablo  Pérez Vargas; Jorge  Zhangallimbay Coraizaca; Alberto  Bazán-Guillén; Pablo Andrés Barbecho Bautista; Mónica  Aguilar Igartua

doi:10.1109/MSWiM67937.2025.11308772

Gap-Fuzzy Adaptive Signal Control: Enhancing Urban Traffic Efficiency

Juan Pablo Pérez Vargas (Primer Autor)
, Jorge Zhangallimbay Coraizaca
, Alberto Bazán-Guillén
, Pablo Andrés Barbecho Bautista
, Mónica Aguilar Igartua (Último Autor)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

Traffic congestion in urban areas has intensified
due to the rapid growth of vehicles, inadequate infrastructure
planning, and unsynchronized traffic signals. This study presents
an adaptive traffic signal control strategy called Gap-Fuzzy,
which combines the Mamdani fuzzy logic controller with a gapout
detection mechanism. The system dynamically adjusts the
duration of the green light based on real-time traffic data, including
queue length and arrival rate. Furthermore, the green phase
is terminated early if prolonged gaps in vehicle flow are detected.
We evaluated the proposed Gap-Fuzzy system using the SUMO
microscopic traffic simulator. The results indicate that it reduced
vehicle waiting times by up to 70% and CO2 emissions by 25%
compared to fixed-time control. In addition, it outperformed the
SUMO actuated controller under low, medium, and high traffic
conditions while maintaining comparable performance under
very high demand. These findings highlight the effectiveness of
integrating fuzzy logic with gap-out detection to enhance traffic
flow and minimize environmental impact.

Idioma original	Inglés
Título de la publicación alojada	2025 International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)
Lugar de publicación	Barcelona, España
Páginas	570-577
Número de páginas	8
ISBN (versión digital)	979-8-3315-6873-3
DOI	https://doi.org/10.1109/MSWiM67937.2025.11308772
Estado	Publicada - 4 nov. 2025
Publicado de forma externa	Sí
Evento	21st Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (PE-WASUN 2025). - Universidad Politécnica de Cataluña, Barcelona, Espana Duración: 27 oct. 2025 → 31 oct. 2025 Número de conferencia: 21 http://pewasun.upc.edu/PEWASUN2025/

Conferencia

Conferencia	21st Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (PE-WASUN 2025).
Título abreviado	PE-WASUN 2025
País/Territorio	Espana
Ciudad	Barcelona
Período	27/10/25 → 31/10/25
Dirección de internet	http://pewasun.upc.edu/PEWASUN2025/

Palabras clave

Intelligent traffic control systems (ITCS)
Fuzzy logic
Gap-out inference
SUMO

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1109/MSWiM67937.2025.11308772

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Pérez Vargas, J. P., Zhangallimbay Coraizaca, J., Bazán-Guillén, A., Barbecho Bautista, P. A., & Aguilar Igartua, M. (2025). Gap-Fuzzy Adaptive Signal Control: Enhancing Urban Traffic Efficiency. En 2025 International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM) (pp. 570-577). https://doi.org/10.1109/MSWiM67937.2025.11308772

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title = "Gap-Fuzzy Adaptive Signal Control: Enhancing Urban Traffic Efficiency",

abstract = "Traffic congestion in urban areas has intensified due to the rapid growth of vehicles, inadequate infrastructure planning, and unsynchronized traffic signals. This study presents an adaptive traffic signal control strategy called Gap-Fuzzy, which combines the Mamdani fuzzy logic controller with a gapout detection mechanism. The system dynamically adjusts the duration of the green light based on real-time traffic data, including queue length and arrival rate. Furthermore, the green phase is terminated early if prolonged gaps in vehicle flow are detected. We evaluated the proposed Gap-Fuzzy system using the SUMO microscopic traffic simulator. The results indicate that it reduced vehicle waiting times by up to 70\% and CO2 emissions by 25\% compared to fixed-time control. In addition, it outperformed the SUMO actuated controller under low, medium, and high traffic conditions while maintaining comparable performance under very high demand. These findings highlight the effectiveness of integrating fuzzy logic with gap-out detection to enhance traffic flow and minimize environmental impact.",

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Pérez Vargas, JP, Zhangallimbay Coraizaca, J, Bazán-Guillén, A, Barbecho Bautista, PA & Aguilar Igartua, M 2025, Gap-Fuzzy Adaptive Signal Control: Enhancing Urban Traffic Efficiency. En 2025 International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM). Barcelona, España, pp. 570-577, 21st Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (PE-WASUN 2025)., Barcelona, Espana, 27/10/25. https://doi.org/10.1109/MSWiM67937.2025.11308772

Gap-Fuzzy Adaptive Signal Control: Enhancing Urban Traffic Efficiency. / Pérez Vargas, Juan Pablo (Primer Autor); Zhangallimbay Coraizaca, Jorge ; Bazán-Guillén, Alberto et al.
2025 International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM). Barcelona, España, 2025. p. 570-577.

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Gap-Fuzzy Adaptive Signal Control

T2 - 21st Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (PE-WASUN 2025).

AU - Pérez Vargas, Juan Pablo

AU - Zhangallimbay Coraizaca, Jorge

AU - Bazán-Guillén, Alberto

AU - Barbecho Bautista, Pablo Andrés

AU - Aguilar Igartua, Mónica

N1 - Conference code: 21

PY - 2025/11/4

Y1 - 2025/11/4

N2 - Traffic congestion in urban areas has intensified due to the rapid growth of vehicles, inadequate infrastructure planning, and unsynchronized traffic signals. This study presents an adaptive traffic signal control strategy called Gap-Fuzzy, which combines the Mamdani fuzzy logic controller with a gapout detection mechanism. The system dynamically adjusts the duration of the green light based on real-time traffic data, including queue length and arrival rate. Furthermore, the green phase is terminated early if prolonged gaps in vehicle flow are detected. We evaluated the proposed Gap-Fuzzy system using the SUMO microscopic traffic simulator. The results indicate that it reduced vehicle waiting times by up to 70% and CO2 emissions by 25% compared to fixed-time control. In addition, it outperformed the SUMO actuated controller under low, medium, and high traffic conditions while maintaining comparable performance under very high demand. These findings highlight the effectiveness of integrating fuzzy logic with gap-out detection to enhance traffic flow and minimize environmental impact.

AB - Traffic congestion in urban areas has intensified due to the rapid growth of vehicles, inadequate infrastructure planning, and unsynchronized traffic signals. This study presents an adaptive traffic signal control strategy called Gap-Fuzzy, which combines the Mamdani fuzzy logic controller with a gapout detection mechanism. The system dynamically adjusts the duration of the green light based on real-time traffic data, including queue length and arrival rate. Furthermore, the green phase is terminated early if prolonged gaps in vehicle flow are detected. We evaluated the proposed Gap-Fuzzy system using the SUMO microscopic traffic simulator. The results indicate that it reduced vehicle waiting times by up to 70% and CO2 emissions by 25% compared to fixed-time control. In addition, it outperformed the SUMO actuated controller under low, medium, and high traffic conditions while maintaining comparable performance under very high demand. These findings highlight the effectiveness of integrating fuzzy logic with gap-out detection to enhance traffic flow and minimize environmental impact.

KW - Intelligent traffic control systems (ITCS)

KW - Fuzzy logic

KW - Gap-out inference

KW - SUMO

UR - https://ieeexplore.ieee.org/document/11308772

U2 - 10.1109/MSWiM67937.2025.11308772

DO - 10.1109/MSWiM67937.2025.11308772

M3 - Contribución a la conferencia

SN - 979-8-3315-7644-8

SP - 570

EP - 577

BT - 2025 International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)

CY - Barcelona, España

Y2 - 27 October 2025 through 31 October 2025

ER -