Revolutionizing Urban Mobility: A Data-Driven Approach to Traffic Forecasting and Management

This paper, titled "Revolutionizing Urban Mobility," focuses on data-driven traffic forecasting and management to address the challenge of sustainable urban transportation. It employs deep learning, particularly LSTM neural networks, to predict traffic conditions and reduce congestion. The research includes comprehensive literature surveys, highlighting the effectiveness of machine learning and deep learning in traffic forecasting. The proposed methodology, based on linear regression, offers promising results with low error rates. Overall, this paper presents a valuable contribution to enhancing urban mobility through data-driven approaches