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Smart city article details

Title	Hybrid Boostrapping Bpnn-Arimax Model For Automobile Petrol Demand Forecasting
ID_Doc	29711
Authors	Subrmanian K.; Faye I.; Thangarasu G.; Kannan K.N.
Year	2024
Published	14th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2024
DOI	http://dx.doi.org/10.1109/ISCAIE61308.2024.10576467
Abstract	This research presents a novel approach to traffic prediction in smart cities using Hybrid Neuro-Genetic Causal Convolution based Autoencoders (HNG-CCA). Urban traffic congestion has become a significant challenge in modern urban planning, necessitating accurate and efficient predictive models. In this work, we propose a hybrid architecture that combines neuro-genetic techniques with causal convolutional autoencoders to enhance the predictive capabilities of traffic patterns. The neuro-genetic approach optimizes the autoencoder architecture, while the causal convolutional layers capture the temporal dependencies inherent in traffic data. Our experiments on real-world traffic datasets demonstrate that the HNG-CCA outperforms existing methods in terms of prediction accuracy with 90%. This hybrid approach not only contributes to the field of traffic prediction but also showcases the potential of combining diverse machine learning paradigms to address complex urban challenges in smart cities. © 2024 IEEE.
Author Keywords	Autoencoders; Casual Convolution; Hybrid Neuro-Genetic; Traffic Prediction

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