Navigating Urban Gridlock: Deep Learning for Predicting City-Wide Traffic Congestion in Smart Cities
Keywords:
Traffic Congestion Prediction, Smart City, Deep Learning, Artificial Intelligence (AI), Traffic Management, and OptimizationAbstract
Traffic congestion prediction has a significant part in managing smart city networks, helping authorities to reduce traffic jams and make transportation systems function well. Even with progress made in deep learning methods, it is common for current ways to experience difficulty when trying to predict congestion patterns accurately. The paper tackles these shortcomings by suggesting a fresh Dynamic Traffic Prediction Network (DTPN) model that blends MobileNet Recurrent Convolutional Network (MRCN) for spatial-temporal traits extraction and Sonar Sweep Optimization (SoSO) as parameter adjustment technique. The newness of our method is in how it can accurately catch the spatial and time-based changes in traffic flow, which results better prediction. When we evaluate using Kaggle datasets, we see that DTPN model does better than other methods for precision, recall, F1-score as well as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The suggested model shows excellent precision of 99%, recall at 98.9% while F1-score is up to 99; these figures are past what Random Forest (RF), XGBoost (XGB), Light GBM (LGBM) and Improved May Fly Optimization with Light GBM (IMFO-LGBM). Moreover, when we compare MAE and RMSE results, it becomes clear that the DTPN model outperforms other methods in various time ranges. This comparison provides more evidence to support the effectiveness of this method for improving prediction accuracy in traffic congestion. It shows promise for a future where urban transport systems are smarter and more efficient.Top of Form
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