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Title	A Convolution Neural Network Based Vanet Traffic Control System In A Smart City
ID_Doc	1098
Authors	Mathiane M.; Tu C.; Adewale P.; Nawej M.
Year	2024
Published	Lecture Notes in Networks and Systems, 825
DOI	http://dx.doi.org/10.1007/978-3-031-47718-8_24
Abstract	Among many methods presented for traffic congestion detection and control, the traffic light control system is effective, however, produces a slew of issues, including excessive delays and high energy consumption. To increase the efficiency, the traffic light length must be adapted dynamically corresponding to real-time traffic. Computer vision techniques have been commonly used for traffic detection. In this article, to regulate the signal for traffic, a Two-Dimensional Convolutional Neural Network (2D-CNN) is constructed for traffic detection and the TensorFlow is used to implement the 2D-CNN. The live traffic data is then used to regulate traffic lights. To validate the proposed method, it is compared with Vehicular Adhoc Network (VANET) adaptive traffic light control, using Simulation of Urban Mobility (SUMO) platform. The simulation’s outcome shows that the proposed method is more effective at controlling traffic signals and reduces traffic congestion. The results showed 96% accuracy on the testing dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Author Keywords	Convolutional Neural Network; Traffic Light Control; Vehicular Adhoc Network

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