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Title	Enhanced Traffic Volume Prediction Using Customized Lstm Models With Bi-Directional Architectures
ID_Doc	23690
Authors	Vijaya Bhaskar S.; Gopala Rao L.V.V.; Gopala Krishna A.
Year	2024
Published	Proceedings of 2024 International Conference on Science, Technology, Engineering and Management, ICSTEM 2024
DOI	http://dx.doi.org/10.1109/ICSTEM61137.2024.10560518
Abstract	This research investigates the efficacy of bidirectional Long Short-Term Memory (LSTM) networks in traffic flow prediction (TFP) for urban transportation systems. It addresses the critical need for precise TFP models to alleviate traffic congestion and optimize transportation infrastructure efficiency. By leveraging LSTM networks with bi-directional architectures, capable of capturing temporal dependencies in sequential data, the study aims to enhance prediction accuracy and robustness. Using real-world traffic data from the Metro Interstate Traffic Volume Dataset, the research develops and evaluates multiple predictive models, including dense neural networks (DNNs), convolutional neural networks (CNNs), and traditional recurrent neural networks (RNNs), alongside customized LSTM models. The findings reveal that the bidirectional LSTM models outperform baseline models, demonstrating substantial improvements in prediction accuracy. Comparative analysis highlights the novel contribution of bi-directional LSTM architectures in enhancing TFP accuracy, particularly in dynamic urban traffic environments. The experimental results underscore the potential of advanced deep learning techniques in developing data-driven solutions for urban transportation management, holding implications for urban planners and policymakers aiming to alleviate traffic congestion and enhance mobility in urban areas. © 2024 IEEE.
Author Keywords	bi-directional architecture; deep learning; LSTM networks; prediction accuracy; traffic congestion alleviation; traffic flow prediction; urban mobility; urban transportation

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