## 2 Literature Review  
### Traditional Travel Time Prediction Methods  
Traditional travel time prediction methods are primarily based on statistics and classical machine learning theory. [PERSON] and [PERSON] [1] first applied Box-Jenkins time series analysis techniques to highway traffic flow and occupancy prediction in 1979, establishing the theoretical foundation for autoregressive integrated moving average (ARIMA) models. Their study analyzed 166 datasets from three monitoring systems in Los Angeles, Minneapolis, and Detroit, finding that the ARIMA (0, 1, 3) model was more accurate than traditional methods such as moving averages and double exponential smoothing in representing highway time-series data.  
Building upon this foundation, [PERSON] and [PERSON] [2] compared the performance of univariate ARIMA, vector autoregressive moving average (VARMA), and spatiotemporal ARIMA (STARIMA) models in urban road network traffic flow prediction. Using data from 25 loop detectors in Athens, Greece, they found that ARIMA, VARMA, and STARIMA models had comparable prediction performance, while historical average models could not cope with data variability. [PERSON] and [PERSON] [3] further applied ARIMA models to urban road travel time prediction, validating the potential and effectiveness of ARIMA modeling in travel time prediction through a case study of Minnesota State Highway 194.  
In addition to time-series methods, machine learning techniques have been widely applied to travel time prediction. [PERSON] et al. [4] first applied support vector regression (SVR) to travel time prediction, comparing it with other benchmark prediction methods using real highway traffic data. The results showed that SVR predictors could significantly reduce the relative mean error and Root Mean Square Error of predicted travel times. [PERSON] et al. [5] proposed a short-term traffic flow prediction model based on support vector machines (SVM). Compared with BP neural network prediction models, they found that this model was superior to BP neural network models in terms of prediction accuracy, convergence time, generalization ability, and optimization potential.  
With the development of ensemble learning theory, tree-based ensemble methods began to receive attention. [PERSON] and [PERSON] [6] used gradient boosting regression trees (GBRT) to analyze and model freight vehicle travel time prediction, utilizing Bayesian optimization for model fitting. Results showed that both pre-departure and post-departure prediction accuracies exceeded 80%. [PERSON] et al. [7] proposed an improved k-nearest neighbor (KNN) model based on spatiotemporal correlation, replacing equivalent distances with physical distances and using Gaussian-weighted Euclidean distance to select nearest neighbors. Experimental results showed that the improved KNN model was more suitable for short-term traffic multi-step prediction than other models.  
[PERSON] et al. [8] proposed a highway travel time prediction framework based on state-space neural networks (SSNN), which exhibited both accuracy and robustness to missing or corrupted input data. Although the SSNN model is a neural network, its design is based on highway segment layout, combining the generality of neural network approaches with traffic-related \"white-box\" design.  
Although traditional methods have achieved certain success in travel time prediction, these methods still have obvious limitations when dealing with urban road travel time prediction. First, statistical models such as ARIMA require data to be stationary or stationary after differencing, while urban road travel time data are often highly non-stationary. Second, shallow machine learning methods struggle to effectively capture complex non-linear relationships and multi-level features in traffic data. Finally, these methods have poor adaptability to sudden traffic events and cannot accurately model the impact of non-recurrent events such as traffic accidents and construction on travel times.