## 2 UAV-Enabled ITS Framework
### Third Layer: Data Collection and Processing
#### 2.3.1 Data Collection  
In ITSs, traffic data are collected, processed, and used for predictions that enable proactive management of the transportation system. Table 4 presents a brief research summary of advancement in UAV-assisted data collection and post-processing methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Research Area** & **Methodology**\({}^{\star}\) & **Findings** \\ \hline [PERSON] et al. [59] & Traffic data & Provides algorithms for internal and external camera parameters calibration and for fusing image distortion & The data platform helps verify traffic simulation models \\ \hline [PERSON] et al. [60] & Traffic parameter estimation & Traffic functionally modeled for estimating microscopic and lane-level macroscopic traffic traffic parameters & The framework successfully extracts microscopic and macroscopic traffic parameters \\ \hline [PERSON] et al. [61] & Traffic parameter estimation & Ensemble classifier and optical flow-based four-step framework for estimating real-time traffic flow parameters & The framework can effectively estimate traffic parameters in congested traffic conditions \\ \hline [PERSON] et al. [62] & Traffic parameter estimation & Kandae-Luces-Tomass (GLT) tracker, k-camera clustering, connected graphs, and four-tuple traffic flow framework & The framework successfully estimates bidirectional traffic parameters \\ \hline [PERSON] et al. [63] & Traffic parameter estimation & Traffic parameter estimation & A traffic parameter estimation framework using training training images processing and vehicle detection methods & Enables extraction of trajectories for each individual vehicle for microscopic traffic flow estimation \\ \hline [PERSON] [64] & Traffic data collection methods & A short overview of different traffic data & Bearing data (FCI) collection methods \\  & & collect methods and traffic parameter estimation methodologies & are cost-efficient and provide better coverage \\ \hline [PERSON] et al. [65] & Traffic parameter estimation & A deep learning-based vehicle counting framework & The framework performs better than other deep neural network-based techniques \\ \hline [PERSON] et al. [66] & Traffic trajectory extraction & An automated UAV video processing framework to extract vehicle trajectories & The framework is supported by a field experiment \\ \hline [PERSON] et al. [67] & Analysis of traffic information & A case study of an automated vehicle trajectory extraction method is shockwave analysis at signalized intersections & Depicts the applicability of the UAV-based traffic analysis system \\ \hline [PERSON] et al. [68] & Traffic and travel time prediction methods & A taxonomy of short-term traffic prediction methods & None of the prediction models perform best in every possible traffic situation \\ \hline [PERSON] et al. [69] & Traffic prediction under abnormal conditions & An online boosting nonparametric regression (OBNR) model for traffic forecasting & The OBNR model performs better than traditional online learning models in abnormal traffic conditions \\ \hline [PERSON] et al. [70] & Traffic flow prediction in intersections & Multiple regression analysis-based traffic characteristics identification and traffic & Reduction in pedestrians’ waiting time by up to 30\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Research summary of data collection and post-processing methods. Traffic data collection methods can be divided into two broad categories: conventional \"in situ\" technologies and floating car data (FCD) technologies [64]. In conventional \"in situ\" technologies, traffic data are collected using sensors located along the roadside. The data collection methods of this category can be further categorized as intrusive and non-intrusive methods. Intrusive methods are direct methods where a sensor and a data collector are placed on or in the road. Different data collection methods, such as induction loops, pneumatic road tubes, piezoelectric sensors, and magnetic loops, fall into this category.