## 2 Literature Review and Conceptual Context
### Traffic Prediction
#### 2.1.1 Using Weather  
More realistic models include the weather variable. Non-deep learning methodologies in this section use K-Nearest Neighbors with a trend adjustment considering past traffic flows to predict a future state [19] or to dynamically adjust parameters according to weather conditions [20]. Random forest is a common out-of-the-bag classification algorithm used to predict traffic states using a congestion index based on historical data [21]. It could also be used in regression problems [22], where weather and other spatiotemporal features are available.  
Recursive Least Square [23] was used for average speed prediction in urban areas with floating cars data. One first instance used Kalman Filter as the prediction model while using RLS to merge average speed with other factors [24]. This study showed that there is a high correlation between the six-time slots previous to the predicted time slot. Another paper addressed the long-term prediction issue by using RLS for parameter estimation and multi-scale correlation for prediction by categorizing weather into categories according to humidity.  
Two other non-deep learning algorithms include Bayesian Networks for a probabilistic approach to traffic congestion [25] and Support Vector Regression with a hybrid evolutionary algorithm implemented with a genetic algorithm to predict traffic flow [26]. However, neither of these methods have proven to have a higher accuracy than neural networks in research. Moreover, traffic prediction requires advanced feature computations since it is a highly complex and non-linear problem that requires advanced feature computations.  
Three deep-learning studies were performed in highway or freeway settings. Traffic prediction was more accurate than SVR, GBRT, and LSTM [27] using a traditional backpropagation-based neural network. It used a layer per term, defining the long term as traffic flow, the medium term as driver's travel habits, and the short term as the numeric traffic flow variations. However, it was not used in an urban setting. Furthermore, it used 36 levels for the weather variable, which questions the learning rate and accuracy of the model, which may be seen as overfitting the dataset. Another article used MLP and mutual information, based on the K value from KNN, to identify the interdependency of historical traffic and future traffic [28]. What made this study relevant is not the use of MLP, which is outdated and less reliable than LSTM. Still, the model can detect inaccurate, noisy, and faulty data--common traits of datasets collected by traffic sensors.  
The third study from this list uses a recurrent neural network and a gated recurrent unit (GRU) [29]. This method discovered non-linear temporal correlations between weather variables and traffic flow but failed to apply the framework to smaller roads. Finally, a regression algorithm starting with different clustering roads using the Gaussian Mixture Model (GMM) was applied in an urban setting and predicted using an artificial neural network [30]. Furthermore, it can perform adaptive forecasting, but it must manually be updated whenever lane reductions occur.  
LSTM NN is the most adopted. LSTM NN has outperformed Deep Belief NNs using precipitation as the weather variable [31]. LSTM NN had better accuracy than decision trees, SVM, and MLP using online traffic and weather data [32]. Rainfall and temperature improve traffic prediction accuracy [33]. A novel application of a model used for traffic prediction without weather variables [18], presented in the previous section, used linear graph transformation to set roads as nodes and adjacency [34], as shown in Figure 4. This architecture took advantage of the GCN trait of being a multidimensional model.