IoT based real-time traffic monitoring system using images sensors by sparse deep learning algorithm

Author(s)

Barbosa, Rodrigo ; Ogobuchi, Okey Daniel ; Joy, Omole Oluwatoyin ; Saadi, Muhammad ; Rosa, Renata Lopes ; Al Otaibi, Sattam ; Rodriguez, Demostenes Zegarra

• Document type: Journal article
• Source: COMPUTER COMMUNICATIONS; v. 210, p. 10-pg., 2023-10-01.

Abstract

Intelligent traffic monitoring systems are necessary and useful tools due to the emerging technologies related to the Internet of Things (IoT) and Artificial Intelligence (AI). The integration of both technologies can facilitate better urban traffic management for fast networks, such as 5G, 5G(+) and 6G environments. However, existing studies focus on complex and expensive solutions or present a latency for generating new training models with accuracy not superior to 98%. Thus, this paper proposes a vehicle identification using a sparse and soft variant of the CNN-based approach, the LightSpaN, which offers a fast training model, without using a complex solution. The effectiveness of the proposed solution is evaluated using the Simulation of Urban MObility (SUMO) tool and a real vehicle traffic implementation using IoT devices. The proposal identified the majority kind of vehicles in a short period of time, faster and more accurately than the related works. The results validated the proposed solution for a real-time traffic monitoring system, presenting an average accuracy of around 99.9% for emergency vehicles. Furthermore, a reduction of both total waiting time and total traveling time was reached by our proposal. (AU)

• FAPESP's process: 18/26455-8 - Audio-Visual Speech Processing by Machine Learning
• Grantee: Miguel Arjona Ramírez
• Support Opportunities: Regular Research Grants