## 3 Analysis and Results
### Analysis of Cell-Based Spatial Separation
#### 3.2.1 Macro-Meso Scale Analysis  
In Figure 11, the time mean speed analysis shows that the speed in the northwestern regions of Gyeonggi Province, such as Gwangmyeong, Siheung, and Incheon, is relatively low, while the speeds in the central and southern regions, as well as the eastern parts of Gyeonggi Province, are higher. This can be attributed to the influence of Incheon Airport, Incheon Port, and nearby logistics hubs. In Figure 12, the space mean speed analysis reveals uneven speed distribution within certain regional cells. The areas from Jonam Junction to IJijk Junction and Soha Junction, as well as the Ansan Junction and Gunja Junction, which connect to the Incheon Port terminal and logistics complexes, show the most significant speed discrepancies. Figure 13 presents an analysis of the similarity between time mean and space mean speeds. With a few exceptions, most regions display heterogeneity in speed. The presence of speed heterogeneity in these sections indicates that sudden driving maneuvers may be necessary, which could serve as important information for ensuring driving safety through predictive driving techniques. \begin{table}
\begin{tabular}{c c c c} \hline
**No** & **Field Name** & **Attribute** & **Applied** & **Remarks** \\ \hline
1 & T\_KEY & Trip key & C86092981018042806421200 \\
2 & CARNUM & Car number & C & Encryption code \\
3 & CAR\_TYPE & Car type & - \\
4 & CAR\_NO & Car registration number & Encryption code \\
5 & TRANS\_REG\_NO & Transportation business registration number & - \\
6 & DRIVER\_CODE & Driver code & - \\
7 & SPEED & Car speed & C & km/h \\
8 & RPM & Engine RPM (Revolutions Per Minute) & r/min \\
9 & BREAK\_ON & Break on signal & & 1 or 0 \\
10 & WGS84\_X & Car latitude coordination & \(\circ\) & WGS84, coordination, X \\
11 & WGS84\_Y & Car longitude coordination & \(\circ\) & WGS84, coordination, Y \\
12 & AZIM & GIS azimuth & \(\circ\) & degree \\
13 & ACCEL\_X & Acceleration & & m/s\({}^{2}\) \\
14 & ACCEL\_Y & Acceleration & & m/s\({}^{2}\) \\
15 & REGION\_CODE & Operation area code & & 41 \\
16 & TIME & Information occurrence date and time & \(\circ\) & 18042809074000 \\ \hline \end{tabular}
\end{table} 
Table 3: DTG raw data and applied data. Figure 11: Homogeneous cells of regional TMS. Figure 12: Homogeneous cells of regional SMS. When a cell showing heterogeneity within a section is included, the result appears smaller than the partition boundary function proposed in this study. However, when the heterogeneous cell is removed, the result satisfies the partition boundary function. This can serve as a criterion for classifying stable and unstable traffic flow sections within road segments. Among the 171 cells, 45 of the last partitioned cells were found to be heterogeneous. While it is possible to create homogeneous cells by further subdividing the heterogeneous sections, as suggested in the methodology, the size of these cells may become excessively small. When a cell that has been subdivided to its minimum size is heterogeneous, it indicates an unstable traffic flow where significant differences in speed occur within the same cell, specifically in areas with large lane-specific speed differences due to external influences. In the case of heterogeneous cells on Highway 1, the difference in speed was not in the direction of travel but was due to lane-specific speed differences. This was particularly evident in the merging and diverging lanes of highway junctions and interchanges, where vehicles slow down when entering or exiting. In this study, DTG data from merging and diverging sections were excluded, but deceleration on acceleration and deceleration lanes was noted. Instead of removing the data, additional lane-specific information can be provided to offer safety information, such as preparing for lane changes or sudden stops. The final cells with large speed variances can be attributed to the influence zones of these highway merging and diverging areas.