## 2 UAV-Enabled ITS Framework
### Third Layer: Data Collection and Processing
#### 2.3.1 Data Collection
In non-intrusive methods, traffic data are collected based on remote observations. Many data collection methods, such as manual counts, passive and active infrared, ultrasonic and passive acoustic, and video image detection, fall into the non-intrusive category. Even though the \"in situ\" technologies have matured and provide high-quality traffic data, they suffer from high installation and maintenance costs, limited coverage of the transportation network, and vulnerability to bad weather conditions. In the FCD technologies, real-time traffic data are collected via mobile phones or global positioning system (GPS) information [64]. In this category, the traffic data collection sensors are drivers' mobile phones and vehicles' GPS information, and the data are collected and stored anonymously in a central processing center. One of the drawbacks of GPS-based FCD technology is limited data coverage, as the old vehicles are rarely equipped with GPS, and new installation costs are high, even though modern vehicles are equipped with GPS. On the contrary, the cellular-based FCD technology has better coverage as mobile phones are cheaper and more common than GPS. Collecting and processing real-time traffic data and subsequently notifying drivers of potential congestion, alternative routes, and incident information greatly enhances the effectiveness and efficiency of the intelligent transportation system (ITS). Still, the real-time tracking of cell phones exposes a considerable security threat for individuals. UAVs offer an effective alternative for traffic data collection over traditional methods because of their spatial and technical advantages. UAV-enabled traffic data collection methods perform better than conventional \"in situ\" methods, as the UAVs are flexible and relatively inexpensive, and can cover a wide range of areas, whereas conventional \"in situ\" methods are fixed-positioned, expensive, and can cover only a limited area in the transportation networks. Compared with FCD technologies, UAV-enabled traffic data collection methods perform better regarding the extent of traffic data coverage and individual security concerns. When vehicles operate in the transportation network without cell phones and GPS, FCD technologies cannot collect their information. Moreover, every piece of data collected via FCD technologies can be traced back to an individual or family, which poses a security issue. On the contrary, UAV-enabled traffic data collection methods can provide complete data coverage of the transportation system without linking the data to any persons or families. Furthermore, UAV-enabled methods can extract microscopic, mesoscopic, and macroscopic traffic parameters. UAVs using digital cameras capture traffic flow data, which covers almost all traffic information, and hence, researchers working on traffic parameter extraction use video data. An example of a scenario in which traffic flow is captured on video using a UAV is shown in Figure 7. In the figure, a UAV hovering over the road uses a digital camera to take a traffic flow video. \begin{table}
\begin{tabular}{c c c c} \hline
**References** & **Research Area** & **Methodology * & **Findings** \\ \hline [PERSON] et al. [71] & Short-term traffic forecast & A long short-term memory (LSTM)-based traffic forecast model & Provids robust traffic volume forecast \\ \hline [PERSON] and [PERSON] [72] & Traffic flow prediction & A framework for correlated traffic prediction using an LSTM network & SerkIMA models \\ \hline [PERSON] et al. [73] & Traffic flow prediction & A deep learning and data fusion architecture for traffic prediction for traffic prediction \\ \hline [PERSON] et al. [74] & Stochastic traffic flow prediction & An adaptive Kalman filter approach for processing SAKHMA + GAKCH structure & Demonstrates improved adaptability in highly volatile traffic situations \\ \hline \end{tabular}
* Notes: SARIMA = Seasonal autoregressive integrated moving average, GARCH = Generalized autoregressive conditional heteroscedasticity.  
\end{table} 
Table 4: _Cont._