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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.02821 |
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| _version_ | 1866912220889219072 |
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| author | Elbasha, Ahmed Mahmoud Abdellatif, Mohammad M. |
| author_facet | Elbasha, Ahmed Mahmoud Abdellatif, Mohammad M. |
| contents | This paper presents a novel AI-based smart traffic management system de-signed to optimize traffic flow and reduce congestion in urban environments. By analysing live footage from existing CCTV cameras, this approach eliminates the need for additional hardware, thereby minimizing both deployment costs and ongoing maintenance expenses. The AI model processes live video feeds to accurately count vehicles and assess traffic density, allowing for adaptive signal control that prioritizes directions with higher traffic volumes. This real-time adaptability ensures smoother traffic flow, reduces congestion, and minimizes waiting times for drivers. Additionally, the proposed system is simulated using PyGame to evaluate its performance under various traffic conditions. The simulation results demonstrate that the AI-based system out-performs traditional static traffic light systems by 34%, leading to significant improvements in traffic flow efficiency. The use of AI to optimize traffic signals can play a crucial role in addressing urban traffic challenges, offering a cost-effective, scalable, and efficient solution for modern cities. This innovative system represents a key advancement in the field of smart city infra-structure and intelligent transportation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02821 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AIoT-based smart traffic management system Elbasha, Ahmed Mahmoud Abdellatif, Mohammad M. Computer Vision and Pattern Recognition This paper presents a novel AI-based smart traffic management system de-signed to optimize traffic flow and reduce congestion in urban environments. By analysing live footage from existing CCTV cameras, this approach eliminates the need for additional hardware, thereby minimizing both deployment costs and ongoing maintenance expenses. The AI model processes live video feeds to accurately count vehicles and assess traffic density, allowing for adaptive signal control that prioritizes directions with higher traffic volumes. This real-time adaptability ensures smoother traffic flow, reduces congestion, and minimizes waiting times for drivers. Additionally, the proposed system is simulated using PyGame to evaluate its performance under various traffic conditions. The simulation results demonstrate that the AI-based system out-performs traditional static traffic light systems by 34%, leading to significant improvements in traffic flow efficiency. The use of AI to optimize traffic signals can play a crucial role in addressing urban traffic challenges, offering a cost-effective, scalable, and efficient solution for modern cities. This innovative system represents a key advancement in the field of smart city infra-structure and intelligent transportation systems. |
| title | AIoT-based smart traffic management system |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.02821 |