## 2 Literature Review and Conceptual Context
### Traffic Prediction  
Traffic prediction is now widely used in cities for infrastructure planning, avoiding congestion, and proactive measures in case of accidents. However, due to its high complexity and the fact that it is affected by external factors, a parametric model is insufficient for acceptable accuracy. Therefore, many nonparametric models have been used for traffic classification and/or regression, such as tree algorithms, different neural networks, and K nearest neighbors. The studies presented in this section use two different data collection techniques (floating car or sensor), two different types of prediction (classification or regression), and two different types of areas (urban or highway/freeway). A summary of these characteristics can be found in Table 2 at the end of this section. Additionally, there has been an increasing trend in traffic prediction studies, as shown in Figure 3, which shows the importance of this research and the search for a higher-accuracy model that can correctly represent real-life scenarios. Long-Short Term Memory (LSTM) neural network is a recurrent network and handles time series using prior timeframes. LSTM is the most popular method. For example, [PERSON] et al. [10] used LSTM to predict traffic congestion to help drivers with their routes. The congestion was determined using real-time images gathered by drones. The drones were able to obtain information regarding speed and volume. LSTM was also used for forecasting using a floating car dataset collected from sensors [11]. The scheme was compared to Support Vector Regression (SVR). LSTM showed better robustness and accuracy. LSTM also has instances, such as [12], that predict multi-step traffic and capture long- and short-term dependencies. Different techniques can be used but their effectiveness is limited, such as the perceptron with several hidden layers (MLP), recurrent networks (RNN), and regression and classification trees. For example, we started with an MLP designed using batches [13]. However, this study was limited from the viewpoint of external variables and other situations. The sub-predictor fusion was achieved with Bayesian models, avoiding errors of relatively larger magnitudes. However, deep learning is still used with RNNs to make advanced regression models, and thus be able to predict traffic on the roads. A tree model was effectively used, and so was the Gradient Boosted Regression method [14]. The problem is that these methods are not applicable in urban areas since the sensors have limitations. More specific models using urban settings are also found in the literature. For example, two studies using urban and road/highway sensor data for training an LSTM NN model [15; 16] take advantage of the LST's sequence-to-sequence (seq2 seq) feature. In addition, these studies emphasize the use of traffic-related characteristics to predict, such as immediate traffic lanes and correlations of prior periods with the existing forecast. Figure 3: Traffic prediction studies throughout the years. Important traffic characteristics are included in network schemes that consider the spatial relationships of different roads. For example, Graph Convolutional Networks (GCNs) exploit the correspondence between a graph of multiple dimensions and a network of roads. Converting the road network into a road graph and its connectivity in a seq2 seq model is a straightforward application [17]. First, seven categories of roads were devised to classify the roads. This categorization was then used as an additional input. More sophisticated systems use GCN, transforming forecasted positions into nodes and contiguousness to other roads as edges, and then converting this model into the input for an LSTM NN, which can be used for short-term projections [18]. This research concluded that graph size does not determine the model's performance. Therefore, GCN can take parts of urban areas and perform specific predictions. We have discussed research that focuses on traffic-related datasets.