# Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data
## 2 Literature Review
These data assume a pivotal role by not only contributing to road and traffic information but also offering insights into the operational conditions of neighboring vehicles [10]. Particularly, DTG data based on satellite positioning systems differ from VDS data collected through devices such as loop detectors and Closed-Circuit Television (CCTV) due to their ability to furnish copious amounts of data, encompassing vehicle locations and instantaneous speeds. While VDS data bear reliability, their utility is constrained by their focus on point information, thereby limiting their ability to capture the dynamic behaviors of vehicles characterized by frequent accelerations and decelerations [11]. Moreover, the generation of diverse traffic information faces spatial limitations, potentially compromising the reliability of the yielded data depending on the traffic environment [12]. The expeditious processing of DTG big data is an imperative facet in collating and presenting micro-segment speed information of vehicles. In this regard, the velocity in processing location-based big data is the most important aspect in deciding whether big data-driven road and traffic information can be delivered in real time [13]. Notably, the potency and rapidity of spatial analysis exert a commanding influence on the processing of location-based big data. However, the present approach of spatial analysis, entailing the connection of current point data with link data, proves inadequate for real-time data provisioning due to its requirement of map matching technology. This arises from the necessity to precisely map coordinates onto the geographical layout and subsequently match them with the requisite buffer area encompassing surrounding links. Moreover, depending on the distance between data points and reference points, varied matching strategies--ranging from point-to-point and point-to-curve to curve-to-curve approaches--to align individual vehicle big data with map data may lead to considerable errors [14; 15]. Therefore, providing accurate speed information based on DTG big data hinges on a comprehensive assessment of accurate and efficient matching procedures between spatial orientation and data representation. Various methodologies have been proposed for generating accurate vehicle speed data. [PERSON] [16] examined methodologies for calculating SMS on expressways, utilizing vehicle trajectory data from radar detectors. [PERSON] [17] discussed the challenges of continuous micro-traffic analysis, contrasting prevalent image-based traffic analysis approaches with a microscopic traffic analysis framework using drone-acquired photographs. These aerial images facilitated the extraction of essential metrics, including speed, traffic volume, and density. [PERSON] [18] employed GPS data to compute SMS for node links, elucidating the nexus between speed and vehicle collision incidents along a segment of the Shanghai trunk route. While the aforementioned studies have contributed commendable methodologies for generating accurate speed data pertinent to their respective datasets, certain limitations persist. First, data procured from roadside stations, loop detectors, CCTV, and probe cars operate within a static link framework relative to the road's central axis. This approach, however, may fall short of representing genuine speed variations or capturing speed homogenous segments characterized by distinctive speed attributes. Fixed sensors like radar and loop detectors solely furnish speed information at designated points, neglecting the broader spatiotemporal distribution of speed [19]. Second, speed data emanating from node links cannot adequately reflect micro-segment attributes. Lastly, methodologies reliant on mobile platform videos face difficulties in acquiring continuous, relevant data and maintaining constant observation [17]. Numerous investigations have explored the geographic alignment of vehicle big data based on Global Navigation Satellite Systems (GNSS). [PERSON] [20] mapped GPS data on a 60 m grid to predict vehicle speeds.