## 2 Literature Review
### Applications of Ensemble Learning in Traffic Prediction  
Ensemble learning completes machine learning tasks by constructing multiple base learners and combining their training results, theoretically achieving significantly superior generalization capability compared to single learners. [PERSON] and [PERSON] [31] employed gradient boosting methods to improve travel time prediction. Results showed that tree-based ensemble methods, by combining simple regression trees with \"poor\" performance, typically produce high prediction accuracy. Compared to other machine learning methods treated as black boxes, tree-based ensemble methods provide interpretable results while requiring less data preprocessing, being able to handle different types of predictor variables, and fitting complex nonlinear relationships.  
[PERSON] et al. [32] proposed a new Bayesian combination method for short-term traffic flow prediction, combining ARIMA models, Kalman filtering, and BP neural networks. The main improvement of this method was changing from considering the entire model prediction values to considering only model prediction values within the sliding time window, making the combination model more sensitive to the perturbation performance of component predictors and able to adjust its weights more rapidly. [PERSON] et al. [33] proposed a Bayesian combination neural network method for short-term highway traffic flow prediction. This method introduced a neural network model that combined predictions from individual neural network predictors according to an adaptive heuristic credit assignment algorithm based on conditional probability theory and Bayes' rule.  
[PERSON] et al. [34] proposed a bus travel time prediction interval model based on road segment sharing, multiple routes' driving style similarity, and the bootstrap method. The model first divides the predicted route into segments, partitioning adjacent stations shared by multiple routes into one segment, then uses hierarchical clustering algorithms to group all drivers of multiple bus routes in that segment according to driving style, and finally uses the bootstrap method to construct bus travel time prediction intervals for different categories of drivers. [PERSON] et al. [35] proposed a multiple-factor based sparse urban travel time prediction method, designing a three-layer artificial neural network model that fuses multiple factors from large-scale probe vehicle data to estimate travel time. The study incorporated different factors to examine their influence on link travel time and validated the approach using historical probe vehicle data from Wuhan, China, from May to July 2014. [PERSON] et al. [36] proposed a method for predicting vehicle travel time on long-distance road segments in Thailand, employing a Self-Attention Long Short-Term Memory(SA-LSTM) model and Butterworth low-pass filter, using historical data from GPS tracking of trucks in Thailand to predict travel time for each road segment.  
Although ensemble learning shows potential in traffic prediction, current research still has some limitations. First, most ensemble methods mainly focus on combining homogeneous models, with less exploration of heterogeneous integration of shallow learning and deep learning models. Second, existing methods lack systematic solutions to the randomness problem in deep learning model training. Finally, there is a lack of systematic theoretical guidance for ensemble strategy selection under different prediction scenarios.