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Title	Traffic Flow In Smart Cities Using Dense Clisteer Humboldt Squid Steerable Nested Convolutional Attention Network
ID_Doc	58568
Authors	Kumar P.V.; Lakshmi B.S.; Mohanapriya R.; Reddy R.A.; Maranan R.; Ramya M.
Year	2024
Published	2024 IEEE 4th International Conference on ICT in Business Industry and Government, ICTBIG 2024
DOI	http://dx.doi.org/10.1109/ICTBIG64922.2024.10911746
Abstract	As urban populations continue to swell; managing traffic flow in smart cities becomes increasingly complex. Efficient traffic management is essential for reducing congestion, improving safety, and enhancing overall urban quality of life. Traditional traffic monitoring systems often struggle with the sheer volume and variety of data, leading to suboptimal traffic management strategies. This research addresses these challenges, this work is proposed. In this manuscript, an advanced traffic flow analysis framework utilizing the Dense Clisteer Humboldt Squid Steerable Nested Convolutional Attention Network (DCli-H2S-NConv-AtNet). In this, the input dataset is taken from INRIX database. Then using Shape-Aware Mesh Normal Filtering (SAMNF) for preprocessing to ensure high-quality, noise-free data. Features are then extracted and classified using an appropriate DCli-H2S-NConv-AtNet for accurately modelling complex spatial and temporal traffic patterns. The rationale behind this framework is to improve the ability to forecast congestion and non-congestion conditions thus improving traffic flow of urban traffic management. Implemented in Python, the DCli-H2S-NConv-AtNet model achieves an impressive accuracy of 99%, significantly improving traffic prediction and management capabilities in smart cities. © 2024 IEEE.
Author Keywords	Dense Nested Attention Network; Humboldt Squid Optimization Algorithm (HSOA); Shape-Aware Mesh Normal Filtering; smart cities; Traffic flow

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