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A vital problem faced by urban areas, traffic congestion impacts wealth, climate, and air pollution in cities. Sustainable transportation systems (STSs) play a crucial role in traffic congestion prediction for adopting transportation networks to improve the efficiency and capacity of traffic management. In STSs, one of the essential functional areas is the advanced traffic management system, which alleviates traffic congestion by locating traffic bottlenecks to intensify the interpretation of the traffic network. Furthermore, in urban areas, accurate short-term traffic congestion forecasting is critical for designing transport infrastructure and for the real-time optimization of traffic. The main objective of this paper was to devise a method to predict short-term traffic congestion (STTC) every 5 min over 1 h. This paper proposes a hybrid Xception support vector machine (XPSVM) classifier model to predict STTC. Primarily, the Xception classifier uses separable convolution, ReLU, and convolution techniques to predict the feature detection in the dataset. Secondarily, the support vector machine (SVM) classifier operates maximum marginal separations to predict the output more accurately using the weight regularization technique and a fine-tuned binary hyperplane mechanism. The dataset used in this work was taken from Google Maps and comprised snapshots of Bangalore, Karnataka, taken using the Selenium automation tool. The experimental outcome showed that the proposed model forecasted traffic congestion with an accuracy of 97.16%.

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Traffic flow prediction in urban areas is essential in the Intelligent Transportation System (ITS). Short Term Traffic Flow (STTF) prediction impacts traffic flow series, where an estimation of the number of vehicles will appear during the next instance of time per hour. Precise STTF is critical in Intelligent Transportation System. Various extinct systems aim for short-term traffic forecasts, ensuring a good precision outcome which was a significant task over the past few years. The main objective of this paper is to propose a new model to predict STTF for every hour of a day. In this paper, we have proposed a novel hybrid algorithm utilizing Principal Component Analysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory (LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCA removes unwanted information from the dataset and selects essential features. Secondly, SAE is used to reduce the dimension of input data using onehot encoding so the model can be trained with better speed. Thirdly, LSTM takes the input from SAE, where the data is sorted in ascending order based on the important features and generates the derived value. Finally, KNN Regressor takes information from LSTM to predict traffic flow. The forecasting performance of the PALKNN model is investigated with Open Road Traffic Statistics dataset, Great Britain, UK. This paper enhanced the traffic flow prediction for every hour of a day with a minimal error value. An extensive experimental analysis was performed on the benchmark dataset. The evaluated results indicate the significant improvement of the proposed PALKNN model over the recent approaches such as KNN, SARIMA, Logistic Regression, RNN, and LSTM in terms of root mean square error (RMSE) of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE) of 2.04%.