## 2 Literature Review
### Data-Driven Methods  
In contrast to model-driven CTMs, some approaches sight to directly derive traffic rules from real traffic data to predict traffic evolution without relying on explicit modeling. In the early research, [PERSON] et al. [18] present the auto-regressive integrated moving average (ARIMA) model by employing the Box-Jenkins approach. The ARIMA constructs the traffic flow as an auto-regressive model for predicting future traffic evolution. On this basis, [PERSON] and [PERSON] [19] develop a seasonal ARIMA to reflect the seasonal patterns of traffic variation. While these regression models can capture the periodicity and trends in time series traffic data, their sensitivity to outliers and noise in actual data may lead to the instability of traffic predictions. Apart from regression, some classic statistic methods are also used to capture evolution patterns from traffic data. For instance, [PERSON] and [PERSON] [20] employ the CTM to model urban roadways and extend the Kalman filter to estimate the traffic state and turning ratios within the cells. [PERSON] et al. [21] adopt Bayesian filtering to fuse CTM and probe vehicle data to establish the state-space model that could estimate traffic states. Similarly, [PERSON] et al. [22] develop a framework that combines the traffic model and Bayesian network to estimate the overall traffic parameters based on partially observed vehicle trajectories and fundamental diagram parameters. These methods effectively manage the outliers and noise in traffic data and reduce the uncertainty of observation errors and model parameters. However, these methods rely on idealized assumptions and possess high computational complexity, which exhibits unsatisfying predictive performances in large-scale complex traffic scenes. As computer science advances, certain machine learning and deep learning algorithms are also utilized for predicting traffic state evolution. For instance, [PERSON] and [PERSON] [23] employ support vector regression (SVR) to search for the optimal separating hyperplane in the feature space and regress a linear model for traffic prediction by minimizing the loss function. Further, [PERSON] and [PERSON] [24] apply the Hidden Markov Model to simulate the traffic evolution patterns between adjacent cells in a 2-dimensional state space consistingof mean and contrast of speed observations. Because of their flexible and adaptive architectures, these machine learning-based models improve their ability to model complex scenarios. Moreover, these approaches no longer solely rely on manual feature selection and construction but automatically extract valuable features from historical traffic data for predictions. Compared to machine learning algorithms, deep learning algorithms utilize the perceptron that possesses stronger nonlinear fitting capabilities, improving performances in traffic predictions. [PERSON] et al. [25] provide a three-layer feedforward neural network (FNN) for traffic speed prediction based on input speed and acceleration. [PERSON] and [PERSON] [26] optimize an architecture that combines a linear model and multiple tanh layers for predicting traffic state evolution. [PERSON] and [PERSON] [27] apply Long Short-Term Memory (LSTM) architectures to accomplish short-term traffic flow prediction and establish the traffic matrices to describe the corresponding traffic states. The models based on deep learning algorithms are adept at extracting implicit high-dimensional features from traffic data and learning their temporal-spatial dependencies and correlations for accurate traffic predictions. However, these methods that solely rely on data training often lack interpretability and are prone to weak generalization ability due to overfitting. In recent research, numerous data-driven approaches combine multiple fundamental models. For example, [PERSON] et al. [28,29] proposed a hybrid interpretable model that combines the fundamental diagram, Markov, and LSTM for short-term traffic flow prediction. [PERSON] et al.