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Transforming Transportation Systems Using Deep Learning Techniques
Abstract
Artificial Intelligence (AI), particularly through the use of Deep Learning techniques, has revolutionized various industries, with transportation being one of the most significantly impacted sectors. AI's ability to handle complex data, such as images and time-series data, has led to the development of intelligent systems that improve transportation efficiency, safety, and management. CNNs excel in tasks like license plate detection for automated traffic surveillance, while RNNs, particularly with LSTM networks, enable real-time traffic flow prediction and congestion management. Furthermore, Generative Adversarial Networks GANs generate high-fidelity traffic simulations, enhancing the testing of autonomous vehicles and infrastructure planning. These Deep Learning models are transforming transportation systems by enabling dynamic, real-time solutions for managing traffic and improving road safety. This chapter explores the fundamentals of AI and Deep Learning, their evolution in neural networks, and their impact on smart transportation through Deep Learning techniques.
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