# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 7 Conclusions and Future Perspectives
To address this problem, in the future, we can adopt the transfer learning method and develop an adaptive transfer learning framework. By pre-training the model on urban data and then fine-tuning it with a small amount of rural data, the prediction accuracy can be improved. [PERSON] et al. [42] adopted a multimodal transfer learning method to transfer knowledge from data-rich cities to data-scarce cities, effectively solving the problem of label scarcity. In addition, the generalization ability of the model in different traffic scenarios, especially in the context of extreme weather conditions, large-scale events, or other unique events, still needs to be improved. A potential solution is to expand the diversity and sources of datasets by merging different types of data (such as traffic flow, speed, travel time, density, weather, population, and image data). The fusion of multimodal data can promote improvements in model performance and expand its applicability [43]. Future research may focus on developing joint learning frameworks that enable stakeholders from diverse data sources and traffic scenarios to collaboratively train a global model, thereby achieving the deep integration of multimodal data and cross-domain knowledge sharing [44]. Under conditions of extreme weather, large-scale events, or other unique circumstances, such joint learning approaches can be used to assimilate heterogeneous data, including traffic flow, speed, and meteorological information and population metrics, effectively mitigating the challenges posed by uneven data distributions and significantly enhancing the model's generalization capabilities and prediction accuracy across various traffic scenarios. In addition, with the continuous development of the field of traffic prediction, an increasing number of traffic prediction models are being proposed. However, the field of traffic flow prediction lacks consistent experimental settings and standardized public datasets. Different scholars use different datasets in their research, and each dataset is affected by various external factors, which complicates the ability to effectively compare the actual prediction performance. Establishing standardized experimental settings and public datasets will greatly accelerate the development of the field of traffic flow prediction and improve the comparability and repeatability of different studies. In addition, long-term traffic flow forecasting is also a significant problem. At present, research on short-term and medium-term traffic flow prediction has achieved significant progress, while research on long-term traffic flow prediction is still relatively limited. Unlike short-term traffic flow prediction, long-term prediction usually lasts for months or even years. Long-term traffic flow prediction faces challenges such as unstable results, high data and model complexity, and complex spatiotemporal dependencies. The question of how to further improve the accuracy of long-term traffic flow prediction models based on existing research is one of the most important research directions for the future. In summary, this study not only provides a new perspective and methodological contribution to the field of traffic flow prediction, but also points out the future research directions. With the continuous development of intelligent transportation systems and the increasing complexity of urban traffic management, traffic flow prediction technology will continue to play an important role. We anticipate that more innovative research results will be obtained in the future to jointly promote the development and application of traffic flow prediction technology. Conceptualization, R.L. and S.-Y.S. ; methodology, R.L. and S.-Y.S. ; writing--original draft preparation, R.L. and S.-Y.S. ; supervision, S.-Y.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data used to support the findings of this study are available from the corresponding author upon request.