## 4 Analysis of Methods for Forecasting Transportation Flow
### Based on Intelligent Algorithmic Models
The inference process in the rule base entails deducing cumulative belief rules through consistent rule activation and the integration of activated rules [49]. The CBBS addresses the limitations of traditional RBR systems and offers greater adaptability across various forecasting scenarios. Fuzzy inference systems are based on specific rules. In [PERSON]'s study, the Gustafson-Kessel (GK) clustering algorithm is employed instead of a traditional fuzzy clustering method. This approach enables the membership values of the input set to be obtained with the GK algorithm within the fuzzy regression functions framework. The analysis results indicate that utilizing the Gustafson-Kessel algorithm significantly enhances prediction performance over traditional rule-based reasoning algorithms [48]. The ANN was first proposed by scholars such as [PERSON] and [PERSON] in the middle of the 20 th century as a nonlinear dynamic network system that was widely connected by many simple processing units. It was applied to simulate the mathematical model of biological neural network behavior characteristics [51; 52], with a good parallel information processing ability and an excellent nonlinear mapping ability. For example, the neural network machine model represented by AlphaGo successively defeated the world go champion and other human giants in 2016 [53]. Furthermore, the AI chat robot model represented by ChatGPT based on a deep neural network showed the vast development potential of neural networks in 2023, proving that the prediction ability of the ANN may be superior to other traditional algorithms [54]. ANNs are divided into multilayer perceptron (MLP), convolutional neural networks (CNNs), recursive neural networks (RNNs), fully convolutional networks (FCNs), long short-term memory networks (LSTMs), BPNNs, and so on based on the different structures of neural networks. Figure 2 presents a schematic of several common neural network structures, while Table 3 displays the comparative results of various commonly used artificial network models. MLP comprises an input layer, a hidden layer, and an output layer. It adopts a full connection of neurons in each layer, with excellent nonlinear mapping and global optimization capabilities. However, it is inefficient in processing multidimensional data. In practical applications, it is affected by the learning efficiency of parameters. Generally, shallow models (no more than three layers) are used [55; 56]. For example, [PERSON] et al. used MLP to estimate near-surface optical turbulence and found that the generalization ability was insufficient when processing a lot of meteorological data [54]. An RNN is characterized by the interconnection of hidden layer neurons. The output of hidden layer nodes depends on the current node input and the previous node value. An RNN can compare the correlation of two events far from the time dimension horizontally. It has a strong ability to extract the characteristics of time series and a good generalization ability, but the sharing of model parameters ignores the independent characteristics of nodes. A gradient disappearance problem seriously reduces the accuracy in dealing with long-term dependence [57; 58]. [PERSON] et al. applied an RNN to the fault detection of nuclear power equipment [59]. It could effectively identify the occurrence of creep by collecting the time series data of rotational speed and vibration. However, the long-term prediction might have led to a decline in prediction accuracy because the independent characteristics of each node were ignored. Figure 2: Schematic structure of several common neural networks. Among them, in the RNN and LSTM networks, \(h_{t}\) denotes the hidden state at time step \(t\), \(x_{t}\) denotes the input at time step \(t\), \(W\) denotes the parameters of the neural networks, and \(C_{t}\) denotes the cell state.