## 4 Analysis of Methods for Forecasting Transportation Flow
### Based on Intelligent Algorithmic Models  
As one of the current mainstream research directions, intelligent algorithms are widely used in all walks of life with their characteristics including their multi-quantization of processing information, high prediction accuracy, and ability to find efficient solutions to complex problems. The research of scholars on intelligent algorithms has focused on analyzing prediction theory, model establishment, and algorithm optimization [40; 41; 42]. Common prediction models include the discrete choice model (DCM), support vector machines (SVMs), decision tree (DT), and the ANN. The DCM describes the phenomenon by using quantitative methods and stochastic utility theory based on the probability. It needs to consider the airport service level and the regional economic development when modelling. Therefore, the DCM is mostly used to study urban transportation mode selection and spatial patterns, and the nested logit model (NLM) is widely used. For example, the NLM can better explain the passenger structureand flow direction under the impact of the economic situation of the airport location and the competition of the surrounding urban airports on the airport passenger flow. However, the prediction process of this method is relatively complicated, without scientific judgment standards for defining the scope of the airport radiation area. Since much data need to be collected, the feasibility needs further improvement [15; 43; 44; 45]. The principle of an SVM is to maximize the geometric interval between positive and negative categories by establishing an optimal hyperplane space for samples. After that, the SVM fits the historical data of baggage flow based on structural risk minimization to predict the current baggage flow. However, it cannot express the complex correlation within the complex data comprehensively and correctly, and the calculation process may lose some important information reflected between the data, so it is not suitable as a long-term prediction tool. For example, the accuracy of the SVM global model established by [PERSON] et al. when predicting the short-term (in 5 min) passenger flow of the subway was significantly higher than the long-term prediction [46]. DT is a simple and interpretable algorithm based on the \"if-else\" rule and is the basic model of all tree models. However, its sensitivity to hyperparameters and simple assumptions leads to its easy overfitting. [PERSON] et al. built a long-term traffic state prediction model using DT and random forest (RF) [47]. This method could effectively capture the internal information of the data but it lacked interpretability. Rule-Based Reasoning (RBR) is a fundamental component of artificial intelligence. It operates by continually using the rules in the knowledge base to match facts and derive conclusions based on those rules. RBR has found applications in various fields, including classification, information sorting, and traffic prediction [48]. RBR employs a rational decision-making design with high interpretability, logic, and transparency, addressing the issue of low interpretability often associated with neural networks. However, RBR struggles with its adaptability to different working conditions and is not well-suited for high-dimensional data. The dimensionality explosion, characterized by an exponential increase in the number of rules with the addition of new rules to the set, poses a significant challenge for machine learning based on RBR [49]. Consequently, in the context of predicting the behavior of complex engineering systems, traditional models may fall short in accurately forecasting future system behavior, necessitating an integrated approach that combines multiple predictive models [50]. [PERSON] et al. have developed a novel RBR system named the Cumulative Belief Rule-Based System (CBBS), which creates cumulative belief rules through the transformation of numerical data and the extended integration of belief rules.