# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 1 Introduction  
Congestion on the road network is, by one definition, reduced quality of service caused by a network carrying more vehicles than it can handle. Congestion estimation is a significant task helping to mitigate the air pollution, by tackling the problem of lower vehicle speeds and increased time delays. If the congestion intensity in the network is known, appropriate optimization, to reduce the negative side effects of traffic, can be applied.  
Estimation of the traffic congestion can be significantly improved by combining data from various sensors and other sources. In the literature, this process is called data fusion (DF). DF of multisource traffic data can be defined as the process of combining data or information to more accurately estimate or predict traffic flow conditions. According to [PERSON] [1] and [PERSON] et al. [2], multisource DF is a technique in which data from multiple sensors are combined to create a synergic process for providing comprehensive and accurate information; in this context comprehensive information means improved, less expensive, and higher quality information. Therefore, DF is a group of techniques by which information from multiple sources is combined to reach a better usability of data and provide a better description of traffic scenario. A general analysis of traffic state tries to identify the traffic flow conditions at which the road would be operating at an optimal level and define congestion as the difference between actual, observed, and optimal conditions. To determine a realistic and accurate state of the traffic flow and predict the travel time, traffic engineers need comprehensive spatiotemporal traffic data. Since every traffic data collection technology has its disadvantages, multisource data collection is often used to cope with the disadvantages and harness the advantages of the used technologies.  
In that regard, the main contributions of this review are:  
* A systematic literature review through a keyword-based search of academic research databases and a systematic identification of highly relevant papers, according to the impact made on scientific community, from the search results.
* Coverage of traffic congestion estimation studies in urban networks based on the data fusion from multiple sensors.
* Analyzation of the objectives of congestion estimation and data fusion, e.g., improving efficiency or accuracy, together with different data fusion methods used according to their performance.  
In this paper, an overview of the most used multisource data collection technologies is given, and methods for DF and congestion indicators for congestion or traffic flow estimation are presented. Its aim is to fill a research gap in the review of methods and technologies for congestion estimation based on multisource data fusion. The main question to be answered is: which data collection technologies were used for data fusion and which data fusion methods are mostly used according to their performance? Section 2 gives the background of the conducted research presented in this paper. In Section 3, a short overview of commonly used quantitative congestion indicators is given. Section 3 gives an overview of commonly used traffic data collection technologies for DF and the advantages and disadvantages of each technology. In Section 4, a representation of different DF methods and techniques used for traffic congestion estimation is provided. In Section 5 a discussion of the recently proposed methods is given, and, finally, Section 6 brings conclusive remarks.