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Abstract ii Abstract In highway management, intelligent vehicle detection and counting are becoming increasingly important as an accurate estimation of traffic density on road congestion reduction. Traffic density estimation is affected by the difficulties of perspective distortion, size change, significant occlusion, and background interference in traffic images. To address the previous issues, a model that enhances the quality of estimating traffic density is developed. The efficientNet fine-tuning architecture is used then, followed by the development of seven dilated convolutional layers to extract the deeper features in the images that maintain the output‟s resolution to generate a high-quality density map. Finally, the vehicle count will be calculated from the high-quality density map. The experimental results indicate that the suggested approach significantly enhances the accuracy of traffic density estimation compared to the existing ones. It achieves promising results on the TRANCOS dataset with a Mean Absolute Error (MAE = 5.23) and Grid Average Mean Absolute Error (GAME (1)) = 5.39, GAME (2) = 5.52, and GAME (3) = 6.40).This is the first phase in this thesis that will be used as input to the second phase, which aims to predict short-term traffic flow to solve the problem of congestion on roadways; that is an issue in many cities, especially at peak times, which causes air and noise pollution and cause pressure on citizens. So, the implementation of Intelligent Transportation Systems (ITSs) is a very important part of smart cities; as a result, the importance of making accurate short-term predictions of traffic flow has significantly increased in recent years. However, the current methods for iii predicting short-term traffic flow are incapable of effectively capturing the complex non-linearity of traffic flow that affects the prediction accuracy. To overcome these problems, a hybrid Long Short-Term Memory (LSTM) and hybrid Gated Recurrent Unit (GRU) neural networks were presented on the basis of the LSTM model and the GRU model. Hybrid LSTM and hybrid GRU models were evaluated in comparison to the other usual models. It is discovered that the hybrid LSTM model and the hybrid GRU model have demonstrably less prediction error than the other models. The experimental results show that the Mean Absolute Error (MAE=7.14 for hybrid LSTM and MAE=6.98 for hybrid GRU), the Mean Absolute Percentage Error (MAPE=16.73 for hybrid LSTM and MAPE=16.50 for hybrid GRU) and the Root Mean Square Error (RMSE=9.74 for hybrid LSTM and RMSE=9.51 for hybrid GRU) on PEMS dataset and introduce promising results on TRANCOS datasets. Hence, a hybrid LSTM and a hybrid GRU can extract the traffic flow temporal features and are able to suddenly capture trend chang |