## 4 Conclusions  
In this study, a cell optimization method was proposed to represent dynamically homogeneous speed sections, regardless of the generation points of the road's nodes and links. The significance lies in the use of raster-type area feature values, allowing for the rapid mapping of large datasets for traffic information analysis.  
The goal was to determine speed-homogeneous sections by progressively splitting and merging cells using time mean speed and space mean speed from speed data. Due to the characteristics of freight trucks, they may travel at 80 km/h in the driving lane or over 100 km/h in the overtaking lane in smooth traffic, resulting in a speed variance of 20 km/h within the same section.  
A microscopic analysis of speed-homogeneous sections was conducted on Highway 1. In the final division of 12 cells, 261 out of 379 cells in the last division were found to be unsuitable in terms of speed homogeneity. This was attributed to vehicles slowing down when entering or exiting via acceleration and deceleration lanes at highway junctions and interchanges, where speed differences occurred between lanes rather than in the direction of travel.  
The final cells with large speed variances can be attributed to the influence zones of highway connection points. In sections with significant speed variances, the influence of the connection point impacts some lanes, allowing for normal driving. Conversely, in sections with small speed variances within a cell, normal driving may be difficult due to the influence of the connection point, traffic volume, or other factors.  
Figure 19: Comparison of processing speed.  
The results of this study verified that the highway traffic information, typically provided in linear form, can be divided into area units using speed information based on real-time GPS coordinates. Although dividing areas based on fixed intervals does not always reveal precise points of speed change, it was found to be possible to establish appropriate divisions based on the size of the cell and the speed-homogeneous sections. Moreover, compared to traditional linear-based analysis methods, this approach significantly improves processing speed as the volume of individual vehicle data increases, offering the advantage of quickly handling large datasets to represent traffic flows in autonomous driving environments.  
However, since this study was conducted using freight truck data, which reflect specific driving characteristics, such as lower relative speeds, the effects of entry/exit lanes, and slow driving at rest areas, the results are not easily generalizable. When applying truck data, they may appear different from the actual traffic situation due to sudden or high-speed changes and differences in lane usage compared to passenger cars. For the accurate analysis of future traffic conditions, further studies using data from a variety of vehicle types, including trucks, are necessary. Additionally, visualizing traffic flow patterns through analyses of non-congested and congested times during the day, as well as congestion patterns on specific days of the week, will be possible. Additionally, improvements in visualization are needed by overlapping the final cell and link information to linearize the homogeneous speed sections.  
Research for this paper was carried out under the KICT Research Program (project no. 20240143-001) funded by the Ministry of Science and ICT.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The author declares no conflicts of interest.