Deep learning based predicting urban traffic congestion with RGB-coded images using GRU-CNN and LSTM.

Road traffic management requires the ability to foresee geographical congestion conditions in an urban road traffic network. The proposed investigation is aimed to envisage the presence of blockage in a specific region of geographical location using gated recurrent unit (GRU) combined with the convolutional neural network (CNN) and long short-term memory (LSTM). The data related to road traffic is extracted from the time—frequency domain of a specific urban road. The collected data is then compressed into a RGB coded image which is the input to the GRU which determines the parameters of traffic blockage in a specific region of the road. The road traffic network features are extracted from those parameters and identified using a CNN's feature extraction technology. We used LSTM model to assess the time series temporal images of the road traffic blockage and to utilize the time division to pool the flow of traffic. For model validation, CTT (Chicago Traffic Tracker) dataset network is chosen. The parameters assessed had ensured highest prediction accuracy of 98.87%, F1-score of 0.98, 0.971 of specificity, 0.987 of recall and 0.978 of precision to forecast the presence road traffic. The proposed model demonstrates a significant advancement in road traffic detection with an accuracy of 98.87%, surpassing existing models that achieve the highest accuracy of 94.3%. This represents a notable improvement of approximately 4.77% over the highest accuracy achieved by the previous models. [ABSTRACT FROM AUTHOR]