## 2 UAV-Enabled ITS Framework
### Third Layer: Data Collection and Processing
#### 2.3.2 Data Processing
The association between a vehicle and its interest points is based on the concept that they should have similar positions and velocities. The vehicle counts in a direction can be calculated from the number of connected graphs in that direction. Traffic parameters such as speed, densities, and volumes for both the forward and reverse directions are calculated in the final step. [PERSON] et al. [63] proposed a new framework for calculating traffic flow parameters. The main steps of this framework are province survey, image processing, vehicle detection, and traffic parameter calculation. For the province survey, a UAV collects traffic flow video data from a region defined by two ground control points. Image processing is carried out on the video data to narrow down the areas of interest of the video frames on the road between two ground control points. Then, in the following step for vehicle detection, a faster region-based convolutional neural network (R-CNN) is used for detecting vehicles from the truncated images, and a simple online real-time tracking (SORT) algorithm based on the Kalman filter is used for vehicle tracking. Finally, traffic flow parameters are calculated in the last step. [PERSON] et al. [65] proposed a robust deep vehicle counting framework (DVCF) for counting different vehicles from UAV video data. The two main parts of the DVCF framework are deep learning-based vehicle detection and vehicle tracking. In the first part, an enhanced single-shot multi-box detector (Enhanced-SSD) was used to produce bounding boxes of vehicles containing information on the type. In the second part, a fast multi-object tracker was applied to the bounding boxes to estimate vehicle trajectory and the number of vehicles by type on the frames. The authors demonstrated that the DVCF method outperforms other existing methods in counting vehicles from high-resolution UAV video. [PERSON] et al. [66] provided a detailed methodological framework for automatically processing UAV video to extract multiple vehicle trajectories, which has applications in traffic parameter estimation and traffic management. The main components of the framework are preprocessing, stabilization, geo-registration, vehicle detection, tracking, and trajectory management. This framework was later used in a case study for identifying shockwaves and estimating flow parameters at signalized intersections [67]. A simple methodological framework for video data processing and traffic parameter extraction is shown in Figure 8.  
Figure 8: An automated UAV video processing and analysis framework [66]. UAV video data processing revolutionizes predictive analysis and decision-making by converting high-resolution aerial footage into actionable insights. One of the critical aspects of UAV-based ITSs is proactive decision-making based on traffic behavior prediction. Proactive decisions help better manage the transportation system in the case of predicted incidents. In a UAV-based ITS, a proactive decision support system should have the following modules: (1) UAV deployment for traffic data collection, (2) traffic data processing for noise reduction and parameter estimation, and (3) data-driven prediction of traffic behaviors. A comprehensive, proactive decision support system for UAV-enabled ITSs is still underdeveloped. Current research often addresses the components--UAV deployment, data collection, and traffic prediction--separately, without integrating all three into a unified framework. A brief description of the traffic behavior prediction techniques is presented in this section. The categories for traffic flow prediction approaches are (1) the parametric approach, (2) the nonparametric approach, and (3) the hybrid approach [68]. In the parametric approach, the structures of the prediction models are defined in advance, and the parameters of the models are obtained from the historical traffic flow data. In the nonparametric case, the prediction model structure and the model parameters are derived from the data.