## 7 Conclusions  
Due to the importance of CF behavior in both macro traffic flow management or micro effective safety functions in ADASs, CF behavior has been extensively investigated to reproduce more realistic human driving trajectories. However, deep knowledge of driving modes in CF and the behavioral differences between modes is lacking, meaning there needs to be a further improvement in the CF modeling accuracy. This study adopted a novel MRF-based time series clustering method to solve the segmentation and clustering of driving modes from massive multivariate naturalistic driving data. A comprehensive comparison of different CF modeling perspectives (DBM and DMBM) is conducted, benefiting from the long-term behavior records of more than 40 drivers. Using the TICC method, five types of CF driving modes are discovered, which can significantly increase the accuracy in reproducing CF behavior with a relatively low clustering BIC criterion. These five data-driven classifications are understandable and basically correspond to the different steps in the human CF process, namely, continuous distance, continuous approach, high-speed stable follow-up, low-speed close-range stable follow-up, and vulgar long-distance follow-up. These five modes describe structural repetitions in human operational behavior. **Precise CF modeling:** Based on the determined CF modes, the driving-mode-based IDM performed better than the driver-based IDM and the hybrid Wiedemann-IDM model, leading by 17.49% and 13.59%, respectively. The lower error compared to the hybrid model further affirms the effect of the TICC algorithm on the recognition of driving modes rather than the mode-division part in the Wiedemann. **Efficient CF modeling:** This is an impressive improvement by the DMBM with far fewer parameters, demonstrating how much potential DMBM has if the driving modes in the CF process can be well recognized. It does not require specific modeling for each driver, whose parameter set is likely not transferable to describe the CF behavior of the other drivers. Meanwhile, the error of the DMBM during the validation process does not increase much compared to that of the calibration, indicating its good transferability for future applications. This study answered an essential question of how the different modes in the CF affect the CF modeling accuracy. By doing so, it expanded the understanding of human CF behavior. On the one hand, it offered an objective perspective on the basic modes involved in the human CF process. On the other hand, it explains the importance of integrating intra-driver variance, especially the behavioral differences between CF modes, into the consideration of the development of CF modeling and personalized advanced driving assistance systems (ADASs). Furthermore, the clustering results of this study may provide support for potential driver recognition since CF behavior can be viewed as a different mode combination of different drivers. However, we should note that this is a preliminary heuristic study on the DMBM. In this study, we focused on discovering the existence of CF modes and their impact on behavior modeling but did not deeply explore the spatiotemporal connection or mathematical quantitative expressions between different modes, which is the direction of our next work. Conceptualization, D.Z., H.R. and L.Y. ; methodology, D.Z. and H.R. ; resources, J.S. ; data curation J.W. and J.S. ; writing, D.Z. ; supervision, J.S. and L.Y. ; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript. This research is jointly sponsored by the National Natural Science Foundation of China (52125208), the National Natural Science Foundation of China (52232015), and the Zhejiang Lab Open Research Project (NO.2021 NLOAB02). The study was quite rigorous in guaranteeing the anonymity of participated drivers in NDS.