# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 6 Discussion  
Research conducted in this review shows that using multisource DF can increase reliability and robustness of estimation, because one data collection technology can contribute traffic information where others are unavailable, unreliable, or ineffective. Multisource  
DF can also decrease costs because usage of several low-budget sensors, in combination with the correct date processing, can achieve the same level of accuracy as the usage of only one more expensive sensor. For multisource DF, authors mostly used a combination of inductive loops, as point sensors, and GNSS data, as area-wide sensors. Potential for determining the most accurate estimation of the state of traffic flow is the use of at least one sensor from each group: point, point-to-point, and area-wide sensors. For example, point sensors (radars or inductive loops) to gather an accurate situation at the road segment or network link, point-to-point sensors (Bluetooth) for enriching area-wide, and area-wide sensors (GNSS data) for gathering a more accurate situation in the traffic network. From the survey made on the selected papers, several potential research directions to address the limitations of the existing methods were identified. Two dominant approaches in estimation of congestion that were mostly used lately are statistical and deep learning methods:  
* Statistical methods can provide insights into the traffic flow conditions but fail when dealing with complex and highly nonlinear data. A statistical method is used either to offer insights about relationships within the data, its structure, or to create a model that can predict future traffic states. Statistical methods have solid and widely accepted mathematical foundations which makes them more \"understandable\" than some deep learning methods. * Much exploited, deep learning approaches create models that are \"intelligent\" and use a large amount of data to get useful insights into the traffic flow and detect various patterns. Deep learning is more flexible than statistics but there is not always a mathematical explanation of why a certain approach works better. Two approaches in using deep neural network (DNN) methods can be observed: (i) combination of image processing-related methods which use convolutional neural networks, and (ii) time-series analysis which uses the long-short term memory network. DNNs have been widely applied to various transportation problems, partly because they are very generic, accurate, and convenient mathematical models able to easily simulate numerical model components. They have been mainly used as a data analytic method because of their ability to work with massive amounts of multidimensional and multisource data. DNN methods are more flexible than statistical methods; the functional form is approximated via learning and not a priori assumed as it is the case with statistics [105]. On the other hand, DNN based models can be computational and memory expensive [106]. Comparing efficiency and performance of these methods is very difficult, if not impossible, and the reason is the usage of completely different testing scenarios for different methods and approaches by different authors. All referenced methods claim rather high accuracy and good performance in the given environment, but it is not clear how they would perform in different conditions. To overcome that, it might be beneficial to test methods using synthetic data which would contain various modalities (sensor data). Only that kind of a test would yield a good comparison of different approaches aiming at solving the same problem. To estimate performance indicators of the traffic flow and to determine congestion, authors mostly used travel time or flow speed. At the time of writing this paper, there was no work trying to fuse congestion indexes mentioned in Table 2 with the above-mentioned methods.