Real-Time Traffic Flow Prediction Model Based on Deep Learning: Taking Smart Highways as an Example

Architectural designers are adopting changes in the sector of traffic management. Advancements and innovations mean a lot in designing predictive models. This is practical in urban areas with high traffic flow. Applying the scenario of smart highways, we develop a model based on deep learning techniques to address challenges faced in this sector. We further our study by utilizing Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) to capture temporal dependencies and spatial correlations within traffic data. A comprehensive dataset, including real-time sensor data from intelligent highways, was used to train and validate the model’s performance. The results of this study show that the deep learning model significantly outperforms traditional methods. This provides superior accuracy and faster response times when predicting traffic conditions. Also, the model demonstrates robustness in adapting to fluctuating traffic patterns, like congestion, accidents, and peak-hour traffic. This makes the model to be highly reliable for dynamic traffic management. By enabling efficient traffic flow predictions, the model holds promise for reducing congestion, enhancing road safety, and optimizing the overall performance of intelligent highway systems. Thus, this paper provides a strong foundation for the implementation of intelligent transportation systems by highlighting the transformative potential of deep learning in future traffic management strategies.