The integration of intelligent systems in smart cities has revolutionized the management of urban infrastructure, with traffic flow prediction being a critical component of this transformation. This paper investigates the application of intelligent systems for real-time traffic flow prediction, aiming to enhance traffic efficiency and reduce congestion in urban environments. By leveraging advanced techniques such as machine learning, data analytics, and artificial intelligence, this research proposes a novel framework for predicting traffic patterns with high accuracy. The framework involves the collection of real-time data from various sources, including traffic cameras, sensors, and historical records, and uses these inputs to train predictive models. The effectiveness of the proposed system is evaluated through experimental analysis, demonstrating improved traffic flow prediction capabilities compared to traditional methods. The outcomes of this study provide valuable insights into the potential of intelligent systems in optimizing urban traffic management and contribute to the development of more efficient and sustainable smart cities.
Taylor, S. Intelligent Systems for Real-Time Traffic Flow Prediction in Smart Cities. Transactions on Applied Soft Computing, 2022, 4, 32. https://doi.org/10.69610/j.tasc.20220919
AMA Style
Taylor S. Intelligent Systems for Real-Time Traffic Flow Prediction in Smart Cities. Transactions on Applied Soft Computing; 2022, 4(2):32. https://doi.org/10.69610/j.tasc.20220919
Chicago/Turabian Style
Taylor, Sophia 2022. "Intelligent Systems for Real-Time Traffic Flow Prediction in Smart Cities" Transactions on Applied Soft Computing 4, no.2:32. https://doi.org/10.69610/j.tasc.20220919
APA style
Taylor, S. (2022). Intelligent Systems for Real-Time Traffic Flow Prediction in Smart Cities. Transactions on Applied Soft Computing, 4(2), 32. https://doi.org/10.69610/j.tasc.20220919
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