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Smart city article details

Title	Traffic Data Augmentation Using Gans For Its
ID_Doc	58555
Authors	Dabboussi A.H.; Jammal M.
Year	2024
Published	Proceedings - 2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2024
DOI	http://dx.doi.org/10.1109/DCOSS-IoT61029.2024.00020
Abstract	Intelligent Transportation Systems (ITS) play a pivotal role in shaping the foundation of smart cities, providing data-driven solutions for traffic management, prediction, and safety. However, these applications often face a significant challenge - data scarcity. Insufficient data limits the effectiveness of machine learning models in the context of ITS. To address this issue, this paper presents a novel data augmentation solution using Generative Adversarial Networks (GANs). By collecting sensor-based traffic speed data with contextual labels and training a GAN-based model to generate realistic traffic data for specific days and times, this research successfully proposes a solution to the problem of data scarcity. The generated data undergoes comprehensive qualitative and quantitative evaluations, demonstrating its potential to enhance ITS applications. Furthermore, the generated data is utilized to augment the training data for multiple traffic prediction models, effectively enhancing their performance. This approach opens new avenues for the development of intelligent and sustainable transportation systems, ultimately contributing to the advancement of smarter and more resilient cities. © 2024 IEEE.
Author Keywords	GAN; Intelligent Transportation Systems; IoT; Machine Learning; Smart Cities; Traffic Prediction; Wasserstein GAN

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