## 2 UAV-Enabled ITS Framework
### Third Layer: Data Collection and Processing
#### 2.3.2 Data Processing
The hybrid approach falls at the intersection of the parametric and nonparametric prediction methods and synthesizes the benefits of both approaches. A taxonomy of the traffic flow and travel-time prediction models is shown in Figure 9.  
[PERSON] et al. [70] used multiple regression analysis to identify the major traffic characteristics responsible for the throughput of traffic intersections. The traffic data were collected using cameras, and the traffic information was extracted from the collected data using an R-CNN. After identifying the key characteristics, a fuzzy logic model was developed to predict the throughput of traffic intersections. The authors used statistical software (Statistical Package for Social Sciences (SPSS)) and the Fuzzy TECH 5.81d Professional Edition computer program for multiple regression analysis and fuzzy model development, respectively. In addition, the authors mentioned that by obtaining traffic characteristics of a particular intersection, the waiting time for pedestrians could be reduced significantly. Forecasting traffic parameters in advance and managing traffic networks accordingly are some of the fundamental goals of ITSs. To forecast traffic parameters in a short time horizon, [PERSON] et al. [71] proposed a deep learning approach for developing a traffic forecast model. The forecast model was a long short-term memory (LSTM) network based on the Internet of Vehicles (ROVs), correlation analysis, and recurrent neural network (RNN). The proposed model differed from conventional forecast models by incorporating a two-dimensional network of memory units in the proposed LSTM network, which accounts for temporal and spatial correlations in traffic systems. The authors concluded that the traffic volume forecast of the proposed model was robust. Over the years, many parametric and nonparametric methods have been developed for traffic prediction, but these techniques are often vulnerable when handling correlated traffic  
Figure 9: Taxonomy of traffic flow and travel-time prediction models [68]. data, as they may suffer from overfitting, fail to account for complex interactions, and struggle with issues like multicollinearity. To make traffic predictions in the presence of spatial-temporal dependencies, [PERSON] and [PERSON] [72] proposed a framework for correlated traffic prediction. This framework exploited a correlated traffic data set to generate two modified data sets based on the data's temporal and spatial dependencies. Then, the temporal and spatial trends were predicted from the associated data sets using an LSTM-based framework. The final traffic prediction was made by taking the average of the spatial and temporal trend predictions. The experimental results showed that the framework would perform better than other naive implementations of the LSTM and SARIMA methods. Weather is a significant factor in transportation systems. [PERSON] et al. [73] investigated the correlation between weather data and traffic parameters and proposed a complete architecture for traffic flow prediction. The prediction architecture incorporated deep belief networks (DBNs) and data fusion and predicted traffic flow in the San Francisco Bay Area using historical traffic and weather data. The performance of the DBNs was compared with state-of-the-art ARIMA and ANN models. In all cases, the DBNs outperformed the two models. One of the limitations of DBNs was that the memory requirement for the supervised training process was substantially more extensive than the memory requirement of the testing process. Proactive traffic management systems need stochastic traffic flow prediction models in the presence of uncertainty. [PERSON] et al. [74] proposed a Kalman filter approach for short-term stochastic traffic prediction and uncertainty quantification. Since both traffic flow predictions and their associated intervals can be derived from the combined SARIMA and GARCH structure, the authors employed adaptive Kalman filters to process this model in real time.