# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 5 Representation of Methods Used in Data Fusion
### DNN Methods  
[PERSON] et al. [PERSON] et al. (2018) investigated the influence of weather conditions on traffic speed in urban conditions. To achieve this, the authors used the Long Short-Term Memory Neural Network (LSTM-NN). In comparison to classical artificial neural networks, LSTMNN has the ability to \"forget\" or store information over a longer period of time. This feature made models using LSTM-NN superior in predicting the speed than SVM, Kalman Filter, and ARIMA. The authors used data obtained from inductive loops and weather data: rainfall, and temperature. For the testing scenario, they decided to use an urban arterial road in Greater Manchester and obtained the best prediction results in terms of minimal absolute error for the model, which used a combination of both weather data and inductive loops. This method outperformed ARIMA by a couple of orders of magnitude. [PERSON] et al. [94] proposed a deep learning-based framework with the integration of road network, weather, and traffic data for predicting the long-term traffic time, called Deep Ensemble stacked Long Short-Term Memory (DE-SLSTM). For the difficulty of predicting the traffic time during congestion, they adopt the \"cost sensitive\" mechanism in the proposed framework to improve the prediction accuracy during rush hours. The proposed framework fits the ground truth better than Google maps and demonstrates good performance. [PERSON] et al. [95] turned to using deep learning architectures to combine text information with time-series data. This approach was used to solve the problem of taxi demand forecasting in New York. By fusing two complementary cross-modal sources of information, the authors showed that the proposed models can significantly reduce the error in the forecasts. Using textual information is rather difficult because it needs to be made understandable for the neural network to process it. The way authors are used here is by converting words into sorted integers, so that closely related words are closer in the number-space. Using the standard representation of successfulness, the authors empirically demonstrated that fusing these two very different data sources leads to significant forecasting error reductions. In the image processing domain, one of the most used approaches is multiple image fusion or multiple feature fusion extraction. [PERSON] et al. [96] preprocessed original images of pavement cracks using different values of standard deviation for the Gaussian blur method. Pavement cracks are detected for every preprocessed image, and the results are then fused into the final image. Often, CNNs are used after fusion for classification or feature extraction purposes. [PERSON] et al. [97] used CNN on the traffic cameras' images to automatically extract the features to estimate the occupancy and the traffic flow. The results are then fused with images that represent density and velocity estimations to achieve final goal of traffic state estimation. [PERSON] et al. in [98] were training a CNN with the multiple features extracted from the images of the drivers in the car. The result was the algorithm for recognition of driving behavior able to detect dangerous actions of the driver. [PERSON] et al. [99] were using a CNN in the fusion of visible spectrum and multi-spectral images. The CNN was trained to detect pedestrians in the images captured in different illumination scenarios. Some authors are using the same dataset but fuse the results of the multiple methods. In [100], the authors are fusing the results of the K-Nearest Neighbor (KNN) with the LSTM. KNN is used to capture spatial features and detect most similar locations. Then, an LSTM is used to predict the traffic flow on the observed locations. In [101], the authors are combining Support Vector Regression (SVR) and the LSTM to detect the abnormal passenger flow on the observed urban rail transit network. The SVR is used to compute a steady passenger flow volume series, and the LSTM to model the large fluctuations in the traffic flow.