## 4 Conclusions  
This study aimed to enhance our understanding of expressway traffic flow by examining the impact zones of traffic accidents, with a particular focus on assessing the effects of accidents to inform prevention and response strategies. A new dynamic segmentation methodology was developed to analyze expressway traffic characteristics, using high-resolution vehicle trajectory data to accurately identify sections of unstable and slow-moving traffic. This approach lays the groundwork for a deeper understanding of traffic accident causes and patterns. While past DTG data were used, the potential use of real-time speed data collected from individual vehicles offers significant advantages, including immediate information availability; reduced data collection and processing costs; and improved analysis of junctions, merging areas, and accident impact zones.  
The methodology used a geohash-based spatial division approach, dividing the target area into multiple levels to facilitate detailed traffic data analysis within each sector. Unstable traffic flows were identified based on speed standard deviation, and the analysis of accident impact zones enabled precise tracking of traffic flow changes following an accident. By comparing pre- and post-accident traffic data at the accident site, the study assessed both the short- and the long-term impacts of the accident on traffic flow, effectively identifying unstable and slow-speed sections, as well as capturing traffic changes triggered by accidents.  
This methodology provides a more granular view of subtle traffic flow changes, offering the precise data needed to reduce traffic accident risks. In stable flow sections, longer data aggregation intervals were applied to maintain consistency, while shorter intervals were used in unstable sections to capture rapid traffic flow variations. The results offer valuable insights into traffic flow alterations post-accident, essential for developing effective traffic management strategies. Additionally, this approach equips traffic authorities to  
Figure 12: Data aggregation time according to changes in stable flow segments.  
better manage accident responses and expedite the restoration of normal traffic flow by predicting congestion and its duration.  
The study's findings can be leveraged to provide real-time, segmented traffic flow information, facilitating average speed predictions within segmented zones. This enables the potential for services through dynamic traffic signs or vehicle navigation systems. In the future, this approach could support autonomous vehicles by aiding AI systems in short-distance traffic condition assessments.  
In summary, this study introduced an innovative methodology for analyzing express-way traffic flows that can be practically applied to traffic system design and management. The proposed methodologies set new standards for traffic safety enhancement and efficient traffic flow management, advancing the precision of traffic data analysis. Moreover, they provide empirical data for traffic planning and policy decisions, ultimately contributing to safer and more efficient traffic system operations.  
In the future, it will be necessary to conduct research using precise data from individual vehicles, such as autonomous vehicles, obtained through communication systems. This would allow for the analysis of quantitative criteria for stable and unstable flows in specific road segments, as well as the quantification of upstream and downstream effects. Such analysis would be valuable not only for examining highways but also for examining merging and diverging areas by road type and developing models to understand the impacts of accidents.  
This study was supported by a research grant from the Ministry of Land, Infrastructure and Transport (20240266-011).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The author declares no conflicts of interest.