## 3 Results
### Traffic Detection Using AI  
Traffic dynamics along National Road 66 and the adjacent rail network were analyzed using YOLO models to enable real-time detection of vehicles and trains. This AI-driven approach provides critical insights into traffic flow patterns, which can be leveraged for both proactive and reactive measures during hazardous situations.  
YOLO Model Implementation:  
Pretrained YOLO models were utilized to identify vehicles and trains with varying detection accuracies influenced by factors such as visibility and object distance from the camera. While the images included in this section (Figures 10 and 11) are from post-rockfall events, these visual examples primarily serve to illustrate the technical implementation of YOLO detection. However, it is important to highlight that YOLO's intended use in this study extends beyond post-event recognition. The model is designed to operate continuously, providing real-time traffic monitoring that can detect anomalies indicative of impending or ongoing hazards ([PERSON] and [PERSON] 2020 [22]).  
For example, video data collected from strategically placed cameras along National Road 66 and the rail network were processed to evaluate traffic patterns dynamically ([PERSON] and [PERSON] 2021 [10]; [PERSON] et al., 2021 [11]). When integrated with real-time hazard monitoring systems, YOLO models can help identify sudden traffic slowdowns, congestion, or deviations, which may signal a rockfall or related hazard. This preemptive detection capability is crucial for issuing timely alerts and informing emergency response strategies, thus enhancing road and rail safety.  
Challenges and Potential Enhancements:  
The deployment of YOLO models under varying environmental conditions--such as reduced visibility or during nighttime--revealed some limitations. For instance, Figure 10 demonstrates long-distance train detection, where accuracy declines with increased detection range and poor lighting. In contrast, Figure 11 showcases more precise vehicle detection in close-range scenarios, where YOLO models perform optimally.  
Figure 9: Floods susceptibility map combined with radar-detected flood areas, illustrating increased susceptibility in the central and southern parts of the gorge.  
Despite these challenges, the real-time monitoring capability of YOLO models is a significant asset for traffic management in hazardous areas. The models can continuously analyze traffic behavior, detect irregularities linked to potential or ongoing rockfall events, and facilitate immediate intervention. This proactive application underscores the value of enhancing traffic detection systems to safeguard both road and rail users in the Jiului Gorge region and similar environments.  
Custom YOLO models were trained for rockfall detection, involving intensive work and precise control over datasets and parameters ([PERSON] et al., 2021 [23]; [PERSON] and [PERSON] 2021 [24]; [PERSON] and [PERSON] 2022 [25]). Training was conducted using Python 3.11 in Google Collab, and the results are depicted in Figures 12-14.  
Figure 11: Road traffic element detection with greater precision due to closer camera proximity.  
Figure 10: Train detection and recognition using YOLO models, illustrating detection from a significant distance with reduced visibility.  
Figure 12: Training results from YOLOv9 model, showcasing classes obtained after training.  
Figure 13: Detection results including several classes, highlighting various rockfall types.