# Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data
## 1 Introduction  
By the year 2022, the Republic of Korea witnessed a total of 196,836 traffic accidents, resulting in 2735 fatalities. Alarmingly, on expressways alone, the accident count reached 4860, with 184 lives lost--nearly 2.7 times the fatality rate compared with other roadways. Because of the median barriers featured by expressways, most of those accidents were side or rear-end collisions (1207 and 1698, respectively), whereas only 40 corresponded to head-on collisions, as shown in Table 1. A significant contributor to accidents is unstable flow (UF) caused by various interferences affecting traffic movement, such as interactions at entrance and exit ramps and accidents, which lead to rapid speed reductions as traffic volume escalates. The prevalence of UF across continuous flow zones like highways underscores its potential role in traffic accidents, necessitating the classification of road segments based on their traffic flow characteristics.  
Currently, traffic information segments (a unit of the road network that provides speed information) are established on nodes and links derived from the road's central axis. Nodes and links are generated at locations such as road intersections, origin and destination points, traffic control sections, points of structural or road management changes, administrative zone transitions, and entry/exit junctures. However, due to these static links, traffic information segments do not correspond to actual speed alteration segments or indicate speed attributes, i.e., despite internal speed differentials within the same segment, they are presented as a singular data entity.  
The rapid advancement of autonomous vehicles and Cooperative Intelligent Transportation Systems (C-ITS) is expected to transform the road/traffic environment into a complex amalgamation of autonomous and non-autonomous vehicles in the near future. This underscores the critical importance of ensuring precision and accuracy in the surrounding vehicle attribute data (such as location and speed) obtained through Vehicle-to-Everything (V2X) communication [1]. Notably, the acquisition of precise micro-segmented speed data is considered a pivotal factor for ensuring safe and comfortable vehicle operation, facilitating autonomous driving, and seamless C-ITS integration in environments where autonomous and non-autonomous vehicles coexist [2]. The provision of precise and reliable information on the speed of proximate vehicles within a relatively large spatial domain is particularly critical to ensuring safety when implementing autonomous driving with high-speed vehicles, given the reduced reaction time available for unforeseen circumstances [3].  
The present study utilizes substantial Digital Tachograph (DTG) data--big data containing location information acquired from individual vehicles--to identify and expound upon UF occurrences, such as instances of speed alterations and segments characterized by velocity disparities between lanes. We introduce discerning criteria for segregating segments characterized by uneven speed distribution, employing space mean speed (SMS) to detect both intra- and inter-vehicle speed differences within a single segment. Furthermore, to provide detailed traffic information, we divide UF segments where speed differences occurred by lane.