## 2 Related Work  
In the past, researchers typically used a parametric approach, which was a data-driven method that emphasized creating statistical and mathematical models for examining time-series relationships within traffic data. The researchers believed that linearity and stationarity were sufficient for predicting future trends in traffic data and used techniques such as the historical average model [25] and error component model [26]. Later, the ARIMA model was introduced, but it has limitations, including a focus on mean values and an inability to predict extremes. Other models, such as seasonal ARIMA [27] and Kalman filter models [28], require large historical datasets and are sensitive to changes in traffic data. The limitation in parametric approaches led researchers to explore nonparametric models such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Bayesian Network (BN), and Artificial Neural Network (ANN). Nonparametric models rely on training data to determine the model structure and the number of parameters. KNN searches for similar data points in the historical database to predict traffic flow while SVM models [29] use the principle of structural risk minimization and are well suited for small samples and high-dimensional nonlinear data. Various SVM-based models, including seasonal SVM, least-square SVM [30], online-SVM [31], and wavelet-SVM [32], have been developed to overcome issues such as overfitting and local minima. BN in [33; 34] considers the causal relationship between random variables and can handle incomplete data through message-passing. All the above-mentioned work requires significant domain knowledge and feature engineering. However, the ANN model in [35] shows its capability to work with multi-dimensional data without any feature engineering and can introduce non-linearity in the learning process but lacks satisfactory performance due to its shallow hidden layers. Other researchers have shifted their focus towards the Deep Neural Networks (DNNs) including RNN and hybrid architecture to predict the traffic information. Recurrent neural networks such as LSTM directly learn the temporal relations from the sequential data and ignore the spatial information. They have been used for various traffic-related applications such as traffic speed prediction [36], traffic flow prediction [22; 37], and traffic congestion prediction [38]. In [39], the researcher uses the LSTM network to predict the road-based congestion information for few roads; the data was collected from an online source. In [37], the researcher presents the LSTM network in a predictive framework based on the correlated traffic data, i.e., the data recorded simultaneously from the different regions of the same transportation networks, which enables the incorporating of both temporal and spatial correlations in the data. In [14], the researcher presents an autoencoder-based architecture that learns temporal information to predict the short-term traffic forecast, however, this approach does not incorporate spatial relation learning. The authors unnaturally compressed the larger traffic map resolution to fit in their computing model; the compressed image was not visually intuitive, and a lot of road information was lost. The authors in [40] introduce a hybrid model by combining KNN-LSTM; the model mines spatio-temporal relationships by selecting similar neighbors in the region and accounting for temporal variability to predict the traffic flow. In [41], the author designs an advanced traffic congestion early-warning system based on an extreme learning machine combined with a modified multi-objective dragonfly optimization algorithm. In [42], the researcher proposes a LC-RNN model, where the LC part uses look-up convolutional operations to extract the spatial information from the adjacent road and recurrent layers to learn the temporal patterns.