Traffic Flow in Smart Cities using Dense Clisteer Humboldt Squid Steerable Nested Convolutional Attention Network

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.