## 1 Introduction  
With the growing issue of severe urban road congestion on a global scale, the prediction of traffic congestion has become an urgent problem that requires swift and effective solutions. Each year the Dutch company TOMTOM releases a notable report focusing on global traffic congestion levels. The data consistently show an upward trend in traffic congestion around the world over the past decade [1]. As a result, accurate prediction of traffic flow in diverse scenarios has garnered significant attention from academia and traffic management organizations as a promising solution. Deep learning models are commonly used for traffic flow prediction. Typically, the process involves preprocessing data, training the model with historical data, optimizing hyperparameters for best evaluation results, and finally using the latest data to make predictions. Given that the Long Short-Term Memory (LSTM) model integrates the characteristics of both long-term and short-term data for prediction, it is highly suitable for application in traffic flow prediction scenarios. Currently, the process of using LSTM for traffic flow prediction still has a few limitations. One is that the presence of excessive and irrelevant data in the input can increase the cost of training and learning. This means that the model needs to process and analyze a large amount of unnecessary information, which can be time-consuming and inefficient. When training against the model, if there are too many input variables, then the computation will increase accordingly, resulting in longer time required for forward and back propagation in each layer. Especially in the fully connected layer, this may cause the model to require more memory to store parameters and intermediate calculation results, thereby affecting training speed [2]. The other is that the relentless pursuit of high accuracy in the LSTM model can result in significant time costs. This can be particularly problematic for practical applications where real-time predictions are required. The model may not be suitable for such scenarios due to the long training and prediction times. To address these limitations, it is important to refine the data preprocessing stage to include only relevant and reliable information. Additionally, exploring optimization techniques that strike a balance between accuracy and computational efficiency can help reduce the time cost associated with training and prediction. The contributions of this paper are as follows:  
* Firstly, the paper highlights our approach to the rational dimensionality reduction of traffic flow data, which helps in reducing the computational complexity [3]. By combining relevant traffic flow parameters and considering realistic road factors, our proposed method effectively integrates and processes the data, resulting in a more efficient computation process. Additionally, the presented closed formulae capture the relationships among traffic flow parameters, further enhancing the data processing approach [4]. * Secondly, three heuristic iterative models are introduced to enhance the MVHS-LSTM model, enabling a more convenient analysis of the impact of various factors such as the number of hidden layer nodes, window length, learning rate, iteration times, and the proportion of training and test sets on model error and computational time. These improvements in the MVHS-LSTM model establish a clear relationship between model error and time complexity, allowing for the identification of an equilibrium point based on the observed tendency [5]. * Finally, extensive simulations are conducted to validate the effectiveness and accuracy of the proposed model. By substituting the optimal solution into the MVHS-LSTM model and comparing its performance with other algorithms, the results demonstrate that the proposed algorithm achieves a balance between the computing time and model error. The simulations not only significantly reduce the computing time but also provide a theoretical foundation for data processing methods.