| _version_ | 1866901796892442624 |
|---|---|
| author | Mory Richard BATIEBO, Mamadou BAKOUAN and Konan YAO |
| author_facet | Mory Richard BATIEBO, Mamadou BAKOUAN and Konan YAO |
| contents | <p>Urban traffic management is a major challenge due to the continuous increase in the number of vehicles, <br>leading to congestion, delays, greenhouse gas emissions, and a decline in quality of life. Traditional traffic lights, based on <br>fixed timers or inductive loops, are no longer suitable for efficiently managing complex and changing urban traffic. <br>Artificial intelligence (AI), particularly convolutional neural networks (CNNs), offers a promising solution. These <br>technologies enable real-time traffic data analysis, learning complex patterns, and adapting traffic light control to the <br>specific needs of each intersection, thus ensuring dynamic and responsive traffic management. <br>This article presents an innovative approach using a CNN model to detect traffic jams in real-time from road surveillance <br>camera data, enabling intelligent decision-making for traffic light regulation. First, we developed a CNN model capable of <br>detecting traffic jams in images. Then, we implemented the proposed model and discussed the results obtained. The model <br>demonstrated a performance of 99% on training data and 97% on test data. Its deployment in real-world conditions also <br>yielded conclusive results.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17577254 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | DYNAMIC TRAFFIC LIGHT OPERATION APPROACH USING CNN Mory Richard BATIEBO, Mamadou BAKOUAN and Konan YAO <p>Urban traffic management is a major challenge due to the continuous increase in the number of vehicles, <br>leading to congestion, delays, greenhouse gas emissions, and a decline in quality of life. Traditional traffic lights, based on <br>fixed timers or inductive loops, are no longer suitable for efficiently managing complex and changing urban traffic. <br>Artificial intelligence (AI), particularly convolutional neural networks (CNNs), offers a promising solution. These <br>technologies enable real-time traffic data analysis, learning complex patterns, and adapting traffic light control to the <br>specific needs of each intersection, thus ensuring dynamic and responsive traffic management. <br>This article presents an innovative approach using a CNN model to detect traffic jams in real-time from road surveillance <br>camera data, enabling intelligent decision-making for traffic light regulation. First, we developed a CNN model capable of <br>detecting traffic jams in images. Then, we implemented the proposed model and discussed the results obtained. The model <br>demonstrated a performance of 99% on training data and 97% on test data. Its deployment in real-world conditions also <br>yielded conclusive results.</p> |
| title | DYNAMIC TRAFFIC LIGHT OPERATION APPROACH USING CNN |
| url | https://doi.org/10.5281/zenodo.17577254 |