# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 5 Representation of Methods Used in Data Fusion
### Statistic Methods  
[PERSON] et al. [79] reported a study on how to explore social media as an auxiliary data source and incorporate it with GNSS probe data to enhance estimation of the traffic congestion. The authors extensively collected tweets that report various traffic events such as congestions, accidents, and road constructions. Next, they proposed an extended Coupled Hidden Markov Model, which can effectively integrate GNSS probe readings and traffic-related tweets to estimate traffic conditions of an arterial network more accurately. The experimental results demonstrated the superior performance of the model by comparison with previous methods. [PERSON] et al. [74] used three different data sources, namely bus-based GPS data, inductive loop detector data, and mobile phone network data that are combined using three different DF techniques. The hybrid method outperforms the weighted mean approach and artificial neural networks to fuse multiple data resources and produce more accurate travel times. The results indicate that fusing multiple data together does not necessarily enhance the accuracy of travel time estimation. Travel time estimation depends on the reliability of individual data sources. Fusing highly correlated data sources can lead to a worse result. The results also show that even in dense urban areas, GPS data, when combined with inductive loop detector data, can provide reasonable travel time estimates of general traffic stream under different traffic states. [PERSON] et al. [84] used a slightly different approach and tried to combine data from social media platforms with GPS data collected from taxies. The authors collected data in 2014, for 19 days, from both sources. The social media data were collected from the largest micro-blog platform in China, Sina Weibo and were filtered through a list of keywords which are somehow related to transport in general. The first step was to match the GPS data on the road network and detect the path with anomalous travel time according to the historical measurements. After that they applied DF of GPS and social media data by selecting the proper messages related to names of nearby roads and landmarks. The social media data, of course, do not contain a proper geolocation, and to solve that problem authors used a rectangular search area of one square kilometer. After statistical analysis, it was concluded that after a non-recurring traffic incident a large number of messages is created, making them a good tool for detection and analysis of unexpected events in the traffic flow. [PERSON] et al. in [PERSON] et al. (2018) proposed an extended generalized filter algorithm for the urban expressway traffic state estimation. To estimate the traffic state, they used multiple sources of data from fixed sensor data (inductive loops or radar data) and GNSS probe vehicle data. [PERSON] et al. [PERSON] et al. (2019) proposed a hybrid data framework; they incorporated GNSS data with loop detector data for real-time travel time estimation and concluded that using fused data gave better results. The authors concluded that better travel time estimation might be achieved by fusing a relatively small amount of probe data other than by doubling the number of loop detectors. [PERSON] et al. [PERSON] et al. (2019) introduced DF to classify the severity of road traffic accidents. The authors described various fusion methods (Dempster-Shafer, Bayesian and Logistic methods) and ensemble algorithms (Bagging and Arcing) to improve the classification accuracy and discrimination power. Fusion algorithms (Bayesian procedure showed best results) display better discrimination power than a single classifier. Dempster-Shafer algorithm appears to improve the classification performance in terms of classification accuracy. Results indicate that a clustering-based classification algorithm works best for road traffic accident classification in Korea. [PERSON] et al. in [PERSON] et al.