# Determining Factors Influencing Operating Speeds on Road Tangents
## 2 Methodology
### Factors Identified--Single-carriageway Road Tangents
The posted speed limit may directly influence operating speeds, often serving as the upper boundary for vehicle speed under free-flow conditions. This factor was obtained from regulatory signage at each study location, ensuring accuracy in reflecting the prescribed speed limits. Moreover, the research has demonstrated that lane width influences driving speed, with wider lanes generally supporting higher speeds due to increased perceived safety margins [54; 55]. Lane width was measured using drone high-resolution imagery, providing precise data on this parameter. Defined by Croatian regulations (Official Gazette 110/2001 and 90/2022) [50], the design speed represents the maximum safe speed under optimal weather conditions. This factor reflects the intent of roadway designers and policymakers to ensure safety and functionality. Data on the design speed (Vp) were derived from the official design documents and engineering standards. The distance from the preceding infrastructure facility (e.g., junction, rest area, bridge, etc.) was included as a factor due to its potential impact on speed changes. These distances were measured using geospatial analysis tools, accounting for features that may disrupt or influence steady traffic flow. Similar to the previous object distance, the following object distance factor accounts for the proximity of upcoming infrastructure that could influence driver behavior and speed. To be more precise, the following object distance refers to the distance from the measurement point to the next significant road infrastructure element, such as an interchange, rest area, or toll station. This parameter is relevant because the presence of such facilities is often announced in advance through vertical traffic signage, typically 1000 to 2500 m before the actual object. As a result, some drivers may begin adjusting their behavior in anticipation, such as changing lanes toward the right (driving) lane or reducing their speed to prepare for an exit maneuver. These anticipatory actions can influence the operating speed measured at the observation point, particularly if the upcoming object is within a range that drivers perceive as necessitating a preparatory response. Therefore, the following object distance serves as a proxy for evaluating the potential behavioral impact of upcoming infrastructure on vehicle speed and lane choice. Measurements were performed using geospatial tools, ensuring consistency with the methodology for the previous object distance. The longitudinal slope of the road might significantly impact vehicle speeds, particularly on extended ascents or descents [47; 48; 49]. This parameter was measured using Google Earth, which provided accurate slope data. The share of heavy goods vehicles (including buses) in traffic flow may affect operating speeds, particularly in the right (driving) lane where heavy goods vehicles predominantly travel. These shares were determined by analyzing the drone video recordings, enabling lane-specific evaluations. The AADT and ASDT factors represent traffic volumes, and were obtained from official datasets. Since the data were available only at the cross-section level (both cariageways), values for the individual cariageways were assumed to be half of the total values. Traffic flow density, defined as vehicles per kilometer per lane, may influence speed by affecting vehicle interactions [56]. Lane-specific traffic flow densities were calculated using the drone video recordings.