# Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data
## 5 Conclusions  
This study introduced a methodology aimed at enhancing the precision of road information for fine-grained micro-segments through the utilization of individual vehicle big data and a criterion for categorizing UF segments characterized by speed variations across lanes. The presented approach involves establishing criteria for identifying segments with uneven speed distributions, leveraging inter-vehicle speed deviation based on the relationship between SMS and TMS, which can indicate the speed difference of individual vehicles within the same segment. Segment divisions were determined by computing standard deviation in speed discrepancies among vehicles through a geohash-coded approach. This approach, in turn, facilitated the identification and classification of UF segments exhibiting lane-specific speed differences, thereby providing detailed traffic information.  
In this investigation, DTG data encompassing vehicle identifiers, coordinates, azimuth angles, and speed measurements from freight vehicles were harnessed within the Gyeonggi region of Expressway No. 50. As a result, a total of 301 segments were identified, including 178 eastbound and 123 westbound segments. UF segments corresponded to partitions falling beyond the reference standard deviation range. Compared with VDS and conzone speeds, the proposed method provided more precise and continuous speed information, surpassing those obtained from conventional link-based approaches.  
A salient outcome of this study resides in its ability to pinpoint specific sections characterized by speed differentials and lane-specific velocity fluctuations. This demonstrated that UF segments can be segmented while under the effect of classification or merging, yielding insights into sudden decelerations or lane-specific slowdowns along expressways.  
Figure 10: Analysis of the precise road network map of the UF segment.  
Such information holds a direct correlation with rear-end and side-impact incidents, positioning it as a valuable tool for furnishing drivers with proactive traffic accident mitigation guidance. Future research endeavors should contemplate the integration of additional data sources, including autonomous vehicles, as well as the exploration of lane-specific speed variation benchmarks based on precise road maps and speed deviation standards for each vehicle in SF according to specific parameters, including road type, lane configuration, vehicle category, and road alignment.  
Due to its capacity to effectively deliver speed information with higher accuracy, the methodological framework introduced in this study for generating micro-segment speed information based on segmentation using individual vehicle big data can be used for real-time micro-segment speed information services. As a result, this approach holds the potential to improve the efficiency of the collection, analysis, segmentation, and dissemination of traffic information, leveraging driving data streams from autonomous, connected, and shared vehicles. Moreover, its applicability extends to supporting secure vehicle operation in dynamic driving environments encompassing both autonomous and non-autonomous vehicles.  
This study was supported by a research grant from the Ministry of Land, Infrastructure, and Transport (20220293-001).  
Not applicable.  
The author declares no conflict of interest.