# A Comparative Study of Ensemble Models for Predicting Road Traffic Congestion
## 2 Literature Review
### Traffic Flow Prediction-Related Studies  
This section reviews related studies on road traffic congestion prediction using ML and statistical analysis methods. Most of the studies considered using traditional ML classifiers.  
A study by [17] proposed that deep learning can predict urban traffic for traffic events mined from Twitter. The study combined traffic Twitter messages with weather information, and it adopted bi-directional long short-term memory and the stacked auto-encoder method. The results proved the value of the proposed approach by refining the model's performance. Another study [18] assessed the effect of a dataset on evaluating traffic prediction approaches. Their study used existing road traffic data, weather data, driving angles, and congestion levels to generate a new dataset, which was used to propose the prediction model. The study used long short-term memory, gated recurrent unit and the stacked auto-encoder methods. Results of the study showed that the new dataset was accurate.  
Another study [19] proposed an approach for monitoring traffic congestion using machine learning to exploit hidden road traffic patterns. The authors managed to identify different time slots in which vehicle traffic congestion occurred, and they monitored average speeds. Yet another study conducted by [20] proposed using deep neural networks (DNN), distributed random forest (DRF), gradient boosting machine, and the generalised linear model to predict short-term traffic flow. The study applied machine-learning approaches to evaluate the use of attributes such as traffic flow, speed, occupancy, and time to reduce the prediction error during traffic flow prediction. On the other hand, [21] only considered artificial neural networks (ANNs) to predict traffic flow, which achieved better results.  
Another study [22] used decision trees, random forest, support vector machine, multi-layer perceptron, and logistic regression to forecast traffic congestion in Internet-based cities. The study results showed that logistic regression performed best compared to the other methods. Furthermore, research by [23] improved traffic flow prediction by considering weather data and using autoregressive integrated moving average (ARIMA), ANN and dynamic Bayesian network (DBN) methods for a deep-learning approach. Thus, in [23], using both the weather and traffic flow data, the results demonstrated the effectiveness of the proposed method. Study [24] used the k-nearest neighbor method to predict traffic flow using short-term data. The study model results proved that the method could predict short-term traffic flow. Finally, a study by [25] used online-SVR, Gaussian maximum likelihood, ANN, and Holt exponential smoothing to predict short-term traffic flow. The study results found that the proposed method achieved the best results compared to the other methods.  
It is noticed that most of the previous studies considered the traditional ML methods to predict road traffic congestion. Previous studies favourably used ANN, deep learning, long short-term memory, and random forest. For example, to assess the performance of these models, commonly used metrics were mean absolute error (MAE), root mean square error (RMSE), mean absolute difference (MAD), mean absolute percent error (MAPE), accuracy, recall, and precision. Ensemble-learning methods are better when compared to the traditional ML methods, as they can improve model prediction and achieve much better performance [26; 27]. It would be beneficial to compare these traditional methods with ensemble methods to investigate potential gains in performance. This study will compare ensemble methods with popular traditional ML methods. The ensemble methods are considered to enhance the performance of the different models.