Data-Driven Decision Support for Climbing and Passing Lane Improvements Using QGIS-Based Highway Segment Analysis

Abstract. This study proposes a data-driven decision-support framework for identifying highway segments suitable for climbing and passing lane improvements using QGIS-based spatial analysis. Focusing on Thailand’s two-lane highway network, the methodology integrates multiple datasets, including digital elevation models (FABDEM), road geometry, and traffic volumes, with the Highway Capacity Manual (HCM) evaluation criteria. Road segments are standardized and segmented into fixed lengths, then aggregated into 500-meter and 3-kilometer groups for analysis of climbing and passing lanes. Key thresholds include road gradient, percentage of heavy vehicles, and traffic volume filters for candidate segments. The results reveal distinct geographic patterns: passing lane opportunities are concentrated in flatter regions with high traffic flow, while climbing lane needs are predominantly located in mountainous northern corridors. By combining open-source tools and national-scale datasets, the proposed framework enables scalable, objective, and transparent planning of auxiliary lanes, thereby supporting safer and more efficient development of highway infrastructure. The approach is adaptable and cost-effective, with potential for application beyond Thailand.