This study addresses the critical challenge of enhancing vehicle classification accuracy in traffic surveys by optimizing the conditions for vehicle axle recognition through artificial intelligence. With current governmental traffic surveys facing issues—particularly the misclassification of freight vehicles in systems employing a 12-category vehicle classification—the research proposes an optimal imaging setup to improve axle recognition accuracy. Field data were acquired at busy intersections using specialized equipment, comparing two camera installation heights under fixed conditions. Analysis revealed that a shooting height of 8.5m combined with a 50°angle significantly reduces occlusion and captures comprehensive vehicle features, including the front, side, and upper views, which are essential for reliable deep learning-based classification. The proposed methodology integrates YOLOv8 for vehicle detection and a CNN-based Deep Sort algorithm for tracking, with image extraction occurring every three frames. The axle regions are then segmented and analyzed for inter-axle distances and patterns, enabling classification into 15 categories—including 12 vehicle types and additional classes such as pedestrians, motorcycles, and personal mobility devices. Experimental results, based on a dataset collected at a high-traffic point in Gwangju, South Korea, demonstrate that the optimized conditions yield an overall accuracy of 97.22% and a PR-Curve AUC of 0.88. Notably, the enhanced setup significantly improved the classification performance for complex vehicle types, such as 6-axle dump trucks and semi-trailers, which are prone to misclassification under lower installation heights. The study concludes that optimized imaging conditions combined with advanced deep learning algorithms for axle recognition can substantially improve vehicle classification accuracy. These findings have important implications for traffic management, infrastructure planning, road maintenance, and policy-making by providing a more reliable and precise basis for traffic data analysis.

PURPOSES: While road networks are becoming increasingly complex, traffic signs are being indiscriminately installed and operated, which makes drivers who depend on traffic signs to reach their destination confused and unable to understand road information efficiently. In order to promptly and exactly guide road users to their destinations, traffic signs should be able to satisfy the functions of connectivity, visibility, and location suitability. However, the results of a site survey shows that most of the traffic signs currently installed in the Jeollanam-do Province do not satisfy these functions. METHODS: This study analyzed the problems of traffic signs after an actual site survey and focused on a total on 9,353 traffic signs and 70 road routes in Jeollanam-do Province. RESULTS : This study analyzed the problems of traffic signs based on their required functions (connectivity, visibility, suitability) and suggested improvements by establishing a guide system that considered the problems found in the study. CONCLUSIONS : The guide system can be utilized as a basic material that provides efficient road information for future installation and maintenance of traffic signs in Jeollanam-do Province.