## 2 Development History of Traffic Prediction
### Deep Learning Methods  
While traditional machine learning models can effectively learn non-linear patterns in data, they still have limitations, such as requiring extensive feature engineering, struggling with high-dimensional data, and not capturing complex temporal dependencies as effectively. With their multi-layer neural network structures, deep learning models provide a powerful capability for handling such data. Due to their numerous layers and large number of parameters, these models are particularly suitable for extracting features from large and complex datasets, thereby achieving excellent predictive performance. This leads to the success of deep learning in multiple fields, such as stock prediction [23], text mining [24], and traffic prediction. Deep learning provides a powerful tool for handling high-dimensional data and learning complex patterns, with Multi-Layer Perceptron (MLP) [25] being one of the most basic deep learning networks that has shown significant potential in traffic prediction. MLP is a feedforward neural network consisting of an input layer, several hidden layers, and an output layer. It is suitable for performing classification and regression tasks, which are crucial in traffic prediction. The goals of traffic prediction include but are not limited to predicting traffic flow, vehicle speed, travel time, and congestion levels. MLPs use backpropagation as a training technique, a supervised learning algorithm that learns the desired output from diverse inputs. Early research applying MLPs to traffic prediction was mainly performed around 1995. For example, [PERSON] et al. [26] applied MLPs to predict highway traffic volume and occupancy, while [PERSON] [27] summarized the potential applications of MLPs in traffic flow modeling. Despite MLP's early widespread application in traffic prediction due to its ability to handle non-linear relationships in traffic data, more powerful and specifically optimized deep learning models are gradually replacing MLPs over time. The subsequent development of RNNs represents a significant evolution of traditional feedforward neural network models, mainly aimed at improving the performance in handling time series data. Traditional feedforward neural networks, such as MLPs, often perform poorly in modeling sequences and time series data, mainly because they lack a component for sustained memory, preventing the network from maintaining the flow of information over time between its neurons. In contrast, the design of RNNs improves the handling of sequential data by introducing internal states or memory units in the network to store past information. The first proposed RNN model consisted of a basic two-layer structure, with a notable feature in the hidden layer: a feedback loop. The addition of this feedback loop was an innovation of RNNs, enabling the network to retain information from previous states to some extent, thus handling sequential data. Although the original RNNs show promise in dealing with data with temporal sequences, their simple design faces significant challenges in practical applications. When training larger RNNs using backpropagation, the issues of vanishing or exploding gradients often arose. This became particularly pronounced when attempting to capture long-term data dependencies, as the error gradients decay exponentially during the backpropagation process, making it difficult for the network to learn these dependencies. To address this problem, a new class of RNN structures, known as gated RNNs, was proposed, which manage long-term dependency information by introducing gate mechanisms, effectively overcoming the vanishing gradient problem. Among these, the two most well-known variants of gated RNNs are LSTMs and GRUs. These models achieve a high capability in capturing long-term dependencies in time series data by incorporating complex gate mechanisms, such as the forget gate, input gate, and output gate, in LSTMs or the update gate and reset gate in GRUs.