Enhancing Autonomous Vehicle Navigation through Deep Learning-Based Traffic Flow Prediction
Keywords:
framework, Mobility, algorithms, autonomous, methodology.Abstract
The phrase "deep learning-based framework for smart mobility" denotes a concept or research paper proposing a framework for traffic pattern prediction using deep learning techniques within the realm of smart mobility. The Autonomous Traffic Prediction: A Deep Learning-Based Framework for Smart Mobility initiative aims to enhance traffic prediction capabilities and develop more intelligent and efficient transportation systems by using the potential of deep learning algorithms. This paper presents a novel Improved Spider Monkey Swarm Optimized Generative Adversarial Network (ISMSO-GAN) method for predicting autonomous traffic in smart mobility. The ISMSO approach enhances the classification efficacy of the GAN. The traffic dataset from the Regional Transportation Management Center for the Twin Cities metro freeways is used to evaluate the efficacy of the proposed method. Noisy data from raw samples is eliminated using the Adaptive Median Filter (AMF). A Kernel Principal Component Analysis (KPCA) is conducted to derive the attributes from the segmented data. The study findings indicate that the proposed technique surpasses previous methods on accuracy, Mean Square Error (MSE), Mean Absolute Error (MAE), and Prediction Rate. Our suggested methodology may significantly improve traffic management and optimize resource allocation.
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