## 1 Introduction  
### Background and Purpose  
Linear spatial information in a Geographic Information System (GIS) refers to spatial data represented in the form of lines, such as roads, rivers, railways, and utility lines. This information primarily consists of one-dimensional linear elements and includes geometric attributes such as location, direction, and length, along with the connectivity of these linear elements. In the case of roads, the basic road information is used to map and analyze or serve traffic information, such as vehicles using the roads. Traffic information is collected using Roadside Equipment (RSE), loop detectors, CCTV, and probe cars, and is provided based on nodes and links along the centerline of roads. Nodes and links are created at road intersections, endpoints, traffic control points, structural change points, administrative boundary change points, road operation change points, and traffic ingress and egress points. Often, these do not coincide with the actual segments of speed changes. In other words, although there can be variations in speed within the same link, it is serviced as a single piece of information. Additionally, traffic analysis based on road geometry uses different methods for large-scale regional analysis, road-specific analysis, and point analysis, often utilizing different base maps such as road maps, numerical maps, and cadastral maps, along with the traffic data collection devices mentioned above. However, with the advent of the fourth industrial revolution and autonomous vehicles, the widespread use of sensors like GPS, LiDAR, radar, and cameras in vehicles has become common. The big data generated from each sensor can be used not only for vehicle operation but also for creating traffic information. Vehicles capable of V2X communication extensively use these data to acquire and generate traffic information, particularly in areas such as Cooperative Intelligent Transport Systems (CITSs) and autonomous driving, with expected expansions in application areas. Analyzing massive amounts of GNSS data can enable real-time services depending on the processing speed, and handling large volumes of data when providing traffic information is crucial as the processing speed significantly impacts spatial analysis.  
Spatial analysis of various forms of location and link data essentially requires map-matching technology, as traditional methods of spatial analysis that connect point data to link data are not suitable. For the effective link matching of values with coordinates, it is necessary to accurately locate positions on the map and apply appropriate buffers around nearby links to re-match them. Given the development of these technologies and data, a unified and efficient linear analysis method is crucial. This approach ensures that spatial data are accurately integrated and utilized, improving the overall effectiveness and utility of geographic information systems in managing and analyzing traffic and other spatially related data.  
This study aims to present an area-based spatial analysis method that, unlike generating nodes and links based on linear-based roads, uses individual vehicle data, particularly truck data, to flexibly apply analytical criteria at the required scale. This method would enable more adaptable and detailed traffic analyses, tailored to specific needs or regions, potentially improving traffic management and planning by leveraging high-resolution vehicle-specific data.