## 2 Development History of Traffic Prediction
### Deep Learning Methods
These mechanisms enable the network to selectively retain or discard information as needed, significantly enhancing the model's ability to capture long-term dependencies in time series data. To the best of our knowledge, in 2015, two pioneering studies applied LSTM to the field of traffic prediction for the first time, specifically for traffic speed prediction and traffic flow prediction. [PERSON] et al. [28] were the first to apply the LSTM model to traffic speed prediction. [PERSON] et al. [29] were the first to propose using the LSTM model for traffic flow prediction. In 2016, [PERSON] et al. [30] were the first to apply the GRU model to traffic flow prediction and compared it with models such as LSTM, ARIMA, and others. Many research works have improved RNNs and are used for traffic prediction. For example, Bidirectional LSTM (Bi-LSTM) [31], two-dimensional LSTM [32], etc., have been utilized. In recent years, in traffic prediction, RNNs have often served as essential components of hybrid deep neural network models, playing a vital role in capturing temporal patterns in traffic data. This reflects the ability of RNNs to understand and predict how traffic flow changes over time, particularly in handling dynamic variations and periodic events. Furthermore, many scholars have researched applying CNNs in traffic prediction. Firstly, a CNN is a commonly used supervised deep learning method. CNNs have achieved great success in image and video analysis, especially in handling data with high spatial correlation. This characteristic makes CNNs an indispensable tool in traffic prediction, especially when dealing with visual data from traffic cameras. Unlike MLPs and RNNs, which mainly deal with numerical time series data, CNNs can directly identify and learn valuable features from raw images without manual feature engineering. Specifically, CNNs can automatically and learn spatial features from input data through convolution and pooling operations while being unsupervised. These features are extracted directly from the raw data, enabling the model to identify previously unknown spatial dependencies. In 2017, [PERSON] et al. [33] were the first to apply CNNs in traffic speed prediction, marking the first instance of treating traffic data in the form of images. Additionally, the introduction of the Conv-LSTM [31] combined the CNN with LSTM to extract spatiotemporal features of traffic flow. Given that the traffic time series data from adjacent nodes on the same link are often correlated, multiple studies have confirmed the effectiveness of convolution-based deep learning models to extract local spatial dependencies from multivariate time series data [34]. In addition to two-dimensional (2D) CNNs for extracting spatial dependencies, one-dimensional (1D) CNNs are usually utilized for capturing temporal dependencies. It is worth noting that in 2018, [PERSON] et al. [35] proposed the Temporal Convolutional Network (TCN). The TCN is a kind of 1D-CNN specifically designed for time series data, aiming to overcome the limitations of traditional CNNs in handling specific problems related to time series. It combines the temporal dependencies handling capabilities of RNNs with the efficient feature extraction capabilities of CNNs. In traffic prediction tasks, TCNs effectively handle sequential data, such as traffic flow and speed, predicting the traffic state for future time intervals. As traffic data often exhibit clear temporal periodicity and trends, TCNs excel at learning these patterns and making accurate predictions with their unique network structure. Some researchers have improved and applied the TCN architecture to traffic prediction tasks, demonstrating good predictive effectiveness [36; 37; 38]. However, in non-Euclidean structures, such as road networks and transportation systems, CNNs struggle to handle the complex relationships between data entities effectively.