## 6 Conclusions  
This paper aims to address the challenge of achieving a balance between accuracy and computing time in traffic flow prediction. The proposed enhanced LSTM model, known as MVHS-LSTM, employs a three-layer architecture to analyze traffic flow variables and MVHS-LSTM hyperparameters using an offline system. Taking into account actual road factors, the variables are carefully selected to determine the validity of traffic flow information and ensure data harmonization. The paper uses the method of multiple regression to calculate the R-squared and selects the most suitable variable for learning from multiple variables through formulas as the input variable for learning. This solved the  
Figure 10: MVHS-LSTM model prediction effect chart.  
problem of the excessive training burden caused by too many input variables in identified challenges. In terms of innovation for LSTM models, the paper uses the iterative method for each parameter, substitutes the calculation error and time into the formula to obtain the minimum value and selects it as the input for the variable. The MVHS-LSTM model undergoes iterative refinement to optimize hyperparameters and establish an equilibrium point between model error and computational time. By implementing the strategy of ending iterations in advance, compared to the PSO-LSTM model, there is no need for a too long training time, which saves the time for model learning and training. Furthermore, multiple sets of comparative tests were conducted, comparing the performance of the MVHS-LSTM model with the ARIMA, SVM, and PSO-LSTM models. The MAPE of the MVHS-LSTM model is 5.8102%, which is 10.4% lower than the ARIMA model, 13.1% lower than the SVM model, and 1.3% lower than the PSO-LSTM model. At the same time, the calculation time is also reduced by 513.4% compared to the PSO-LSTM model. The results demonstrate that our approach achieves a harmonious balance between prediction accuracy and computational time, enabling rapid responsiveness to real-time traffic flow variations and carrying practical significance for future applications. By analyzing and predicting data on traffic flow, congestion patterns, and road usage, traffic management departments can better optimize traffic signals, route planning, and traffic control strategies, thereby alleviating congestion, reducing traffic accident risks, and improving the overall traffic operation efficiency of roads. At present, this paper only focuses on predicting traffic flow on a single road segment. Subsequent research can synchronize predictions in space based on the topology map of the road segment, thereby radiating to the entire city and bringing new thinking and innovation to urban-level traffic prediction. At the same time, this paper does not consider the spatial relationship between road segments, specifically the external spatial characteristics of traffic flow data. In future research, we will further consider the spatial relationships between road segments within the traffic flow data, aiming to enhance the theoretical framework.  
[PERSON] designed the system model and revised the manuscript. [PERSON] drafted the manuscript, evaluated the experiments, and drew the figures. [PERSON] supervised the manuscript and improved the architecture. All authors have read and agreed to the published version of the manuscript. This research was funded by The National Natural Science Foundation of China under Grant No. 62201350; The Shanghai Sailing Program under Grant No. 21 YF1432800; The National Key Research and Development Program of China under Grant No. 2022 YFB4501704. Not applicable. Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare that they have no known competing interests or personal relationships that could have appeared to influence the work reported in this paper.