# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 5 Conclusions  
The proposed risk assessment method effectively integrates car-following and lane-changing behaviors, offering a more precise evaluation of collision risks in mixed traffic flow. Empirical validation demonstrates that the method can effectively identify high-risk scenarios. The study reveals that the degradation effects of CAVs, PRT, and penetration rates have significant impacts on traffic safety, specifically in the following aspects:  
1. When the TTC is set to 2 s, the collision risk threshold of \(R_{\text{total}}\) is determined to be 0.49, providing a reliable benchmark for identifying critical situations;
2. Considering CAV degradation, when the penetration rate of CAV is between 0.4 and 0.6, collision risks increase by an average of 15%, highlighting the safety challenges of intermediate CAV adoption rates in mixed traffic flow. \(R_{\text{total}}\) takes into account multiple factors, such as PRT, and demonstrates relatively sensitive variations, providing a more effective representation of overall changes in traffic flow safety;  
Figure 15: Impact of lane-changing modes on various safety metrics. 3. At lower PRTs, penetration rates have a more significant impact on collision risks, whereas at higher PRTs, the impact decreases, as vehicle coordination is insufficient to mitigate the elevated risks caused by delayed PRTs. Compared to other metrics, \(R_{\text{total}}\) exhibits superior applicability and stability, making it more suitable for evaluating the long-term comprehensive risks and macro-level trends of traffic flow;
4. In lane-changing scenarios, the degradation of the FVT contributes the most to collision risks, emphasizing the importance of vehicle degradation location and its combinations in overall risk assessment. This study offers practical recommendations for public traffic management, including the development of real-time risk prediction and warning systems to accurately identify high-risk car-following and lane-changing behaviors. It also suggests enhancements in lane management strategies and dynamic speed control measures, with a focus on addressing safety challenges in mixed traffic flows with intermediate CAV penetration rates. Special emergency response measures should also be established to mitigate risks arising from CAV degradation. Moreover, collaboration with vehicle manufacturers and traffic management agencies is essential to promote the deep integration of CAV technologies with transportation infrastructure, thereby enhancing overall traffic safety. Future research should explore the real-time implementation of the proposed risk index within intelligent transportation systems and examine its effectiveness under different levels of vehicle automation and connectivity. Additionally, the applicability of this method should be extended to urban roads, intersections, and other complex traffic environments. Incorporating environmental factors, such as weather conditions and road surface quality, could further optimize the predictive capabilities of the model. Furthermore, dynamic risk thresholds and their integration with real-time control algorithms should be investigated to better support traffic safety management across diverse scenarios. These advancements will provide robust technical support for the global optimization and dynamic risk management of intelligent transportation systems. Conceptualization, M.Z. and J.Y. ; methodology, M.Z. ; software, M.Z. and J.Y. ; validation, M.Z., J.Y. and X.D. ; formal analysis, M.Z. ; investigation, M.Z. ; resources, X.Y. ; data curation, M.Z. and J.Y. ; writing--original draft preparation, M.Z. and J.Y. ; writing--review and editing, M.Z., J.Y. and X.D. ; visualization, M.Z. and J.Y. ; supervision, X.D. and X.Y. ; project administration, X.Y. ; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by Zhengzhou Major Science and Technology Innovation Project, grant number 2021 KJZX0060, and the National Natural Science Foundation of China, grant No.