# Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data
## 2 Literature Review  
A junction is a location where vehicles merge and sort, prompted by competitive conflicts arising from divergent traffic demands. Primarily established to link the traffic streams of an expressway and a standard route, this segment exhibits a higher frequency of lane changes compared with the main expressway artery, resulting in inherently unstable traffic flow, heightened congestion, and a higher propensity for accidents. The most common type of connections are entrance and exit ramps; the impact area is calculated based on the junction's geometric design, road alignment, and lane count, whereas service levels are determined based on traffic volume [4]. Efforts to mitigate UF have encompassed the integration of Variable Message Signs (VMS) and density metrics within a congestion \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accident Type** & **Road Type** & **Number of Accidents** & **Number of Deaths** & **Number of Injured** \\ \hline \multirow{8}{*}{Head-on collision} & National highway & 7849 & 110 & 12,590 \\  & Provincial road & 5376 & 88 & 8707 \\  & Metropolitan city road & 28,854 & 112 & 42,697 \\  & City road & 25,989 & 207 & 38,946 \\  & County road & 3568 & 48 & 5587 \\  & Expressway & 1333 & 10 & 2374 \\  & Unknown & 3588 & 15 & 5255 \\ \hline \multirow{8}{*}{Rear-end collision} & National highway & 4521 & 66 & 8403 \\  & Provincial road & 2052 & 27 & 3736 \\  & Metropolitan city road & 11,917 & 48 & 19,860 \\  & City road & 8820 & 69 & 15,075 \\  & County road & 694 & 16 & 1176 \\  & Expressway & 1943 & 100 & 4852 \\  & Unknown & 1257 & 3 & 2091 \\ \hline \multirow{8}{*}{Etc./unknown} & National highway & 4729 & 41 & 7093 \\  & Provincial road & 2171 & 18 & 3301 \\ \cline{1-1}  & Metropolitan city road & 17,893 & 75 & 25,026 \\ \cline{1-1}  & City road & 13,276 & 69 & 18,890 \\ \cline{1-1}  & County road & 739 & 8 & 1097 \\ \cline{1-1}  & Expressway & 1374 & 27 & 2775 \\ \cline{1-1}  & Unknown & 3733 & 14 & 5191 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Traffic accident statistics in 2022.  
prediction model that leverages the cumulative moving average of vehicle speed arrays [5]. A vehicle trajectory model rooted in Vehicle Detection System (VDS) data was used in the construction of a dynamic Origin-Destination (O-D) prediction model to explore congested traffic flow dynamics [6]. Factors that can explain lane interdependencies were identified in the study \"Development of Impulse Propagation Model between Lanes through Temporal-Spatial Analysis\" [7]. This led to the development of a lane impact propagation model, employing multiple regression analysis to quantify impact based on the analyzed segment and lane. Notably, prior research grappled with the challenge of precisely timing UF instances through the examination of detector data from junctions, such as entrance and exit ramps. Given the inherent variation in speeds within a traffic stream, an accurate single depiction of the speed of a traffic stream necessitates understanding speed distribution, typically approximated by a normal distribution. The average speed is usually divided into two components: time mean speed (TMS) and SMS. TMS represents the arithmetic mean of speeds exhibited by vehicles traversing a point or short road segment over a defined interval. On the other hand, SMS quantifies the sum of all vehicles passing through a specific segment within a specific period, divided by the distance traveled [8]. Consequently, the speed of vehicles in a specific segment is expressed by the relationship between TMS and SMS, with UF surfacing when individual vehicle speeds diverge from a given value. Because they are fixed on individual vehicles and provide crucial information about their operation, DTG data emerge as a promising resource for generating finer-grained micro-speed information [9]. The widespread integration of diverse sensors within vehicles has ushered in an era of location-based big data, sourced from individual vehicle sensors.