## 7 Conclusions  
This paper provided a comprehensive review of the application of RNNs in traffic prediction. Specifically, we first outlined the history of traffic prediction, classified existing traffic prediction methods based on existing input sequence representation methods and forms, and summarized in detail the research progress on predicting tasks with different input sequence lengths. The discussion on input data representation emphasized the importance of choosing appropriate data formats to optimize model performance. Additionally, we summarized the classical models, RNNs, and their variants, as well as hybrid models combined with RNNs applied to traffic prediction. Next, we presented the representative results for seven traffic prediction tasks, providing a comprehensive overview of research progress in different application scenarios. Finally, we discussed the comparison between RNNs and popular deep learning models and the major challenges. The key findings of our review are listed as follows:  
1. RNNs are extensively utilized in traffic prediction. In processing various types of traffic data, RNNs not only efficiently handle time series data independently but also serve as temporal feature extraction modules when combined with models, such as CNNs and GNNs, for spatiotemporal data processing. Furthermore, RNNs play a significant role across seven sub-areas within traffic prediction. 2. RNNs are expected to continue being preferred models for future traffic prediction tasks due to their advantages and will not be replaced by transformers. We conducted a comparative study using real-world metro smart card datasets for short-term passenger flow prediction. This allowed us to directly compare the predictive performance of RNNs (particularly LSTMs) with other models in a real and specific environment. The results showed that, despite the presence of more advanced transformer models, RNNs demonstrated superior performance. This finding underscores the importance of selecting the appropriate model based on the characteristics of different datasets and available resources, as sometimes simpler models can provide more accurate and efficient predictions. This paper served as a valuable resource for participants seeking a comprehensive understanding of traffic prediction, enabling them to identify areas of interest quickly. It offered a thorough reference for researchers, facilitating exploration and fostering the advancement of related studies in the field. By providing an in-depth analysis of the application of RNNs in traffic prediction, this review aimed to promote innovation and development, encouraging the design of more effective traffic prediction models and integrating advanced methodologies. Ultimately, it aspi to contribute to the progress and refinement of traffic prediction research, benefiting theoretical exploration and practical application. **Author Contributions:** Conceptualization, Y.H. and K.-L.T. ; methodology, Y.H. and P.H. ; software, W.H. ; validation, Y.H., P.H. and W.H. ; formal analysis, P.H. ; investigation, P.H. ; resources, Y.H. and Q.L. ; data curation, Y.H. and W.H. ; writing--original draft preparation, Y.H. and P.H. ; writing--review and editing, Y.H., L.L. and K.-L.T. ; visualization, P.H. and W.H. ; supervision, K.-L.T. ; project administration, Y.H. ; funding acquisition, Y.H. and Q.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This study was supported by the National Natural Science Foundation of China (72301180); the Guangdong Basic and Applied Basic Research Foundation (2021A1515110731); the Shenzhen Science and Technology Program (RCBS20231211090512002); the Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety (R202203); Natural Science Foundation of Top Talent of SZTU (GDRC202126); a grant from the Department of Education of Guangdong Province (2022 KCXTD027); and the Guangdong Key Construction Discipline Research Ability Enhancement Project (2021 ZDJS108).