## 1 Introduction
### Related Research
Additionally, in spatial analysis, route-based node-link network analysis cannot overcome the fixed link size determined by traffic flow characteristics, and its processing speed limits real-time analysis. Map matching using individual vehicle data involves various algorithms. The point-to-point method matches point-to-point locations and is quick and simple but highly sensitive to errors. The point-to-curve method connects GNSS coordinates to the nearest road segment using buffer calculations and is the most commonly used method in road networks. Another approach is the curve-to-curve method, which matches entire curves, but it is less common. In addition, topology-based map-matching algorithms can encounter errors in complex urban road networks, particularly in areas with short, adjacent links. These errors are exacerbated when abnormal location data are involved. The probabilistic algorithm accounts for these by using all candidate links within an error ellipse surrounding the GNSS position [10; 11; 12]. However, these map-matching algorithms differ significantly from the point-to-area algorithm, which maps the positional data to an area rather than directly to a link on the road, considering the essential role of spatial searches. In addition to map matching based on the geographic application of GNSS data, various studies have used GNSS location data for traffic information generation, investigation, and analysis. For example, [PERSON] [13] conducted an individual traffic survey using GPS sensors, while [PERSON] [14] focused on predicting truck speeds under non-congested conditions. Additionally, [PERSON] [15] conducted research on generating local traffic flow and speed model data using Internet-based GPS vehicle records. However, all these studies have used linear-based matching methods in their approach. In a similar traffic-data-mapping study, [PERSON] [16] used GNSS data at a macro level to support transportation and urban planning, while [PERSON] [17] applied GNSS data at a meso level to reflect travel speed patterns on road networks. Furthermore, [PERSON] [18] focused on micro-level applications, such as updating network structures and attributes and exploring driving routes. Though numerous GNSS-based traffic information analyses have been conducted at the micro level, the comprehensive analysis of road network information using large-scale GNSS data across macro, meso, and micro levels is a key distinction of this research. Additionally, while previous grid-based GNSS data applications have used predefined grid sizes, this study differs in its approach by using a dynamic grid system. Geohash is a technique that converts geographic coordinates, specifically latitude and longitude, into a single number to enhance spatial information search speeds in large-scale spatial data. This geocoding method using base-32 notation, and later expanded with variations, such as base-64 and Hilbert encoding [19]. Geohash-based coding has been widely applied in GNSS systems embedded in smartphones, which collect location data. These systems have been used as alternatives to traditional surveys [20] and for analyzing travel patterns [21; 22; 23; 24]. However, examples of using this method in linear-based traffic information analysis are still limited. In this study, unlike the traditional node-link system that analyzes traffic flow only at fixed link levels, I directly analyzed the characteristics of road traffic flow, such as unstable traffic, slow speeds, and high speeds. The node-link system sets nodes at merging/diverging sections and conducts traffic analysis using two links, with each link's average speed calculated based on point speeds measured by the installed Vehicle Detection System (VDS).