## 2 Literature Review
### Applications of Deep Learning Methods in Travel Time Prediction
[19] proposed an end-to-end deep learning travel time estimation framework called DeepPTE, which captures spatial correlations through geo-convolution operations that integrate geographical information into classical convolutions, while capturing temporal dependencies by stacking recurrent units on geo-convolutional layers. In recent years, more advanced deep learning architectures have been proposed. [PERSON] et al. [20] proposed the Fusion Convolutional Long Short-Term Memory Network (FCL-Net), which addresses spatial dependencies, temporal dependencies, and exogenous dependencies simultaneously by stacking and fusing multiple convolutional LSTM layers, standard LSTM layers, and convolutional layers. [PERSON] et al. [21] proposed a multi-step deep learning algorithm for travel time prediction that starts with data preprocessing, then enhances data by incorporating external datasets, and applies extensive feature learning and engineering. [PERSON] et al. [22] proposed a deep neural network-based traffic flow prediction model (DNN-BTF) that fully utilizes weekly/daily periodicity and spatiotemporal characteristics of traffic flow, introducing an attention-based model that automatically learns to determine the importance of past traffic flow. [PERSON] et al. [23] proposed a Deep Irregular Convolutional Residual LSTM network model (DST-ICRL) for urban traffic passenger flow prediction, which models passenger flows between different transportation lines as multi-channel matrices analogous to image RGB pixel matrices. Recent studies also include the Bi-Directional Isometric Gated Recurrent Unit (BDI-GRU) proposed by [PERSON] et al. [24], the Spatial-Temporal Attention Neural Network (STANN) for deep learning traffic flow prediction proposed by [PERSON] et al. [25], and the Short-Term and Long-Term Integrated Transformer (SLIT) proposed by [PERSON] et al. [26]. [PERSON] et al. [27] proposed a GraphSAGE-based Dynamic Spatial-Temporal Graph Convolutional Network (DST-GraphSAGE), and [PERSON] et al. [28] proposed a deep spatial-temporal travel time prediction model based on trajectory features. [PERSON]'s [29] recent study compared the performance of three machine learning algorithms--k-nearest neighbors (k-NN), LSTM, and transformer--in travel time prediction, with results showing that transformer achieved the lowest prediction error (10.3%). [PERSON] et al. [30] conducted a comprehensive review of the applications of deep learning algorithms and classical models in traffic prediction, reviewing 111 important research works since the 1980s. Although deep learning methods have demonstrated powerful capabilities in travel time prediction, challenges remain: high training costs, difficult hyperparameter optimization, randomness issues, and poor interpretability. Particularly, the randomness problem in deep learning models--even when using the same network structure and hyperparameters, different prediction results may be produced in different training iterations--increases the difficulty of model selection and evaluation. While our literature review has identified these emerging architectures, this study focuses on establishing a systematic comparison framework using well-established deep learning methods (LSTM-DNN) alongside traditional shallow learning approaches. This methodological choice allows us to address the critical randomness issues in deep learning model training and develop robust ensemble strategies. The comprehensive evaluation framework established in this study provides a solid foundation for future incorporation of transformer-based and graph neural network approaches when appropriate datasets and computational resources become available.