## 2 Related Works
Subsequently, they applied a fusion model of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict the subsequent day's traffic flow. [PERSON] et al. [29] initially applied a fast Fourier Transform and its inverse within the time-series correlation module to decompose traffic flow data. They then used an attention-based encoder-decoder graph convolutional model to predict multi-step future traffic flow. [PERSON] et al. [30] used the Empirical Mode Decomposition method to segment traffic flow into components of varied frequencies, leveraging the self-similarity of each to choose appropriate prediction models for short-term forecasting. Compared with wavelet decomposition, fast Fourier Transform, and Empirical Mode Decomposition, Variational Mode Decomposition autonomously identifies the requisite number of modal decompositions, preventing modal aliasing and thus potentially enhancing post-processing prediction accuracy. Research by numerous scholars has demonstrated significant improvements in model prediction accuracy following the application of Variational Mode Decomposition to time-series data. For instance, [PERSON] et al. [31] initially smoothed traffic flow data using Variational Mode Decomposition, inputted the resulting subsequences into an LSTM network prediction model, optimized the model parameters using the dung beetle algorithm (IDBO), and aggregated the subsequences' predicted values to derive final predictions. [PERSON] et al. [32] applied Variational Mode Decomposition to segment historical traffic flow data into K components, determined by varying K sample entropy values. They then smoothed each component using wavelet thresholding to obtain denoised subsequences, predicted each using an LSTM network, and aggregated these predictions for final results. [PERSON] et al. [33] addressed the non-stationarity and complexity of traffic flow data by decomposing it into K subsequences using Variational Mode Decomposition and constructing a CNN-GRU network within the Keras framework to extract temporal features. Given the uncertainty of traffic flow data influenced by external factors like travel demand and traffic signals, [PERSON] et al. [13] proposed a hybrid model incorporating Variational Mode Decomposition for traffic flow prediction. Building on the preceding analysis, employing suitable data decomposition techniques coupled with integrating various prediction methods can substantially elevate the prediction model's performance and enhance its future event forecasting accuracy. Data decomposition simplifies and reduces its noise by segmenting the original dataset into several sub-signals, facilitating more efficient extraction of essential features and patterns. Furthermore, selecting algorithms or models that align with the data's characteristics is crucial to establishing a mapping relationship between the dataset and the predictive target. Deep learning algorithms for model optimization and enhancement further boost the prediction methods' accuracy and applicability. This holistic approach augments the prediction model's efficacy and offers a more accurate and reliable method for forecasting future events in complex data scenarios.