## 2 Literature Review
### Applications of Deep Learning Methods in Travel Time Prediction  
The rise of deep learning technology has brought new opportunities to the field of traffic prediction. [PERSON] et al. [9] applied deep learning methods to traffic research in 2014, proposing a deep architecture consisting of deep belief networks (DBN) and multi-task regression layers for traffic flow prediction. This study used DBN for unsupervised feature learning and multi-task regression at the top layer for supervised prediction. Experimental results showed that this method improved performance by approximately 5% compared to state-of-the-art methods. With the development of recurrent neural network technology, [PERSON] and [PERSON] [10] proposed a short-term traffic flow prediction model based on long short-term memory recurrent neural networks (LSTM RNN). This model utilized three multiplicative units in memory blocks to dynamically determine optimal time lags, overcoming the limitation of traditional models that require predefined input historical data lengths. Compared with models such as random walk (RW), support vector machines (SVM), feedforward neural networks (FFNN), and stacked autoencoders (SAE), the LSTM RNN model demonstrated higher accuracy and generalization capability. [PERSON] et al. [11] proposed a deep learning traffic flow prediction method based on stacked autoencoders that considered spatial and temporal correlations, using stacked autoencoders to learn generic traffic flow features and training in a greedy layer-wise manner. Experiments showed that this method had superior performance in traffic flow prediction. [PERSON] et al. [12] pioneered a convolutional neural network (CNN)-based approach, treating traffic learning as image processing by converting spatiotemporal traffic dynamics into images describing temporal and spatial relationships of traffic flow through two-dimensional spatiotemporal matrices. Experimental results on real traffic networks in Beijing showed that this method improved average accuracy by 42.91% compared to other algorithms. [PERSON] et al. [13] compared traffic flow prediction models based on LSTM networks and gated recurrent units (GRU). Experimental results showed that RNN-based deep learning methods such as LSTM and GRU outperformed ARIMA models in performance. [PERSON] et al. [14] proposed a deep learning method based on error-feedback recurrent convolutional neural network (eRCNN) structure, integrating spatiotemporal traffic speeds of continuous road segments as input matrices and explicitly utilizing implicit correlations between nearby segments to improve prediction accuracy. For the specific needs of travel time prediction, [PERSON] et al. [15] established a series of long short-term memory neural networks with deep neural layer (LSTM-DNN) combination models using 16 hyperparameter settings and conducted detailed performance investigations on a 90-day travel time dataset from the Caltrans Performance Measurement System (PeMS). This study found that different hyperparameter settings caused deep learning models to exhibit different characteristics, providing important insights for model structure optimization. [PERSON] and [PERSON] [16] developed a deep learning architecture combining L1 regularized linear models and a sequence of tanh layers for traffic flow prediction. The main contribution of this study was developing an architecture capable of capturing sharp non-linearities due to transitions between free flow, breakdown, recovery, and congestion. [PERSON] and [PERSON] [17] proposed two deep learning models based on LSTM neural networks and self-attention mechanisms--hybrid LSTM and sequential LSTM--for jointly predicting next location and travel time. [PERSON] et al. [18] addressed the bus travel time prediction problem by proposing a novel segment-based approach that combines real-time taxi and bus datasets, capable of automatically dividing bus routes into dwelling and transit segments. [PERSON] et al.