## 1 Introduction
[17] proposed an innovative congestion prediction method using ship trajectory dynamic big data analysis and multi-scale feature analysis to solve the problem of congestion in the maritime network. [PERSON] et al. [18] proposed a novel hybrid model (CPQDELM) for short-term traffic flow prediction with the mountainous scenic highway S321k51 dataset as an example. [PERSON] [19] proposed SSA-BiLSTM-BP-Elman integrated model for predicting peak hour traffic flow to solve the problem of congestion on peak road sections. [PERSON] et al. [20] investigated deep learning-based traffic flow prediction method to provide a more accurate basis for sensing the congestion condition of the route network. [PERSON] et al. [21] proposed a novel traffic flow prediction model called anomaly detection probability (ADP) attention bidirectional long and short-term memory (BiLSTM) model, which is used for a comprehensive simulation study of the Shanghai-Chongqing Expressway using the measured traffic flow data. Short-term traffic flow prediction is provided for highway sections. In addition, short-time traffic flow also shows the characteristics of nonlinear, stochastic, real-time, and spatiotemporal correlation, and the current research is still deficient in the selected indexes [22; 23]. [PERSON] et al. [24] constructed a short-term traffic flow prediction model using short-term and short-term memory networks and used variational mode decomposition to solve the mode mixing problem, but the study gave less consideration to the short-term traffic flow indicator system, such as climatic conditions and public transportation status. And no robustness analysis was performed. [PERSON] et al. [25] proposed a new hybrid deep learning model based on the attention mechanism, which uses a multilayer hybrid architecture to extract spatiotemporal nonlinear features. However, the effect of climatic conditions on traffic flow is complex, and climatic conditions are not included in the metrics in the study. [PERSON] et al. [26] proposed a long-time traffic flow prediction model based on variational modal decomposition (VMD) and autocorrelation mechanism. The model mainly relies on historical traffic flow data and does not consider the influence of external factors such as weather, holidays, and traffic accidents. This may lead to the bias of prediction results in special cases. [PERSON] et al. [27] proposed a short-term traffic flow prediction algorithm based on cloud modeling and fuzzy inference system, which is used to solve the problem that the existing research ignores the uncertainty of traffic flow prediction that affects the accuracy and robustness of the model. However, as the number of fuzzy clusters increases, the complexity of the model increases significantly, leading to an increase in training time and computational cost. Therefore, to improve the prediction accuracy of non-trunk roads and side roads with small traffic flow, this paper proposes a short-time traffic flow prediction model for SVR based on associated roads, using association rules to analyze the data to find the other roads that are strongly related to each road, and using this as the data basis to establish the SVR model, and reconstructs the index system for short-time traffic flow prediction [28; 29; 30]. Association rule analysis can help identify other roads that are associated with changes in flow on the target road. The flow data of these related roads can be used as inputs to the SVR model, thus enriching the dataset and improving the generalization ability of the model. With association rule analysis, such correlations can be identified and incorporated into the inputs of the SVR model to improve prediction accuracy. Association rule analysis can reduce the dependence on the parameter selection of the SVR model, thus reducing the sensitivity of the model to parameter selection.