<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the growing number of city vehicles, traffic management is becoming a persistent challenge. Traffic bottlenecks cause significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, wait times for commuters at traffic signal points are not reduced. The proposed methodology employs artificial intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in shorter vehicle waiting times.</p>
</abstract>

With the growing number of city vehicles, traffic management is becoming a persistent challenge. Traffic bottlenecks cause significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, wait times for commuters at traffic signal points are not reduced. The proposed methodology employs artificial intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in shorter vehicle waiting times.

AI-Based Yolo V4 Intelligent Traffic Light Control System

Department of Computer Science and Engineering

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"AI-Based Yolo V4 Intelligent Traffic Light Control System"}

With the growing number of city vehicles, traffic management is becoming a persistent challenge. Traffic bottlenecks cause significant disturbances in our...

AI-Based Yolo V4 Intelligent Traffic Light Control System

Online veröffentlicht: 20. Okt. 2023

Seitenbereich: 53 - 61

Eingereicht: 26. Apr. 2022

Akzeptiert: 28. Juli 2022

DOI: https://doi.org/10.14313/jamris/4-2022/33

Schlüsselwörter
Traffic jams, traffic light system, traffic management, intelligent monitoring, signal switching algorithm, artificial intelligence

© 2022 Boppuru Rudra Prathap et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

AI-Based Yolo V4 Intelligent Traffic Light Control System

Online veröffentlicht: 20. Okt. 2023

Seitenbereich: 53 - 61

Eingereicht: 26. Apr. 2022

Akzeptiert: 28. Juli 2022

DOI: https://doi.org/10.14313/jamris/4-2022/33

SchlüsselwörterTraffic jams, traffic light system, traffic management, intelligent monitoring, signal switching algorithm, artificial intelligence

© 2022 Boppuru Rudra Prathap et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Traffic jams, traffic light system, traffic management, intelligent monitoring, signal switching algorithm, artificial intelligence