# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 5 Representation of Methods Used in Data Fusion  
Challenges of DF are imperfection, inconsistency, confliction, alignment and correlation, and heterogeneity of the type of data. Researchers are dealing with these challenges to improve efficiency and quality in traffic estimation, classification, and prediction tasks. In [69] and [71], to combine multisource data, the authors proposed a coupled matrix and tensor factorization scheme named TCE_R. A method called search tree-based pattern mining is proposed to efficiently discover which road segments, geographically close to each other, are likely to experience the co-occurrence of traffic congestion. Recursive Kalman filter-based approaches provide a solution for traffic state estimation and DF. However, when data cannot be straightforwardly aligned over space and time, equations become computationally expensive. Therefore, the authors propose three alternative DF approaches that solve this problem and are tailored to fuse different traffic sensor data. The so-called PISCIT, FlowResTD, and Treiber-Helbing filter (EGTF) are able to fuse multiple data sources, as long as for each of these it is possible to estimate under which traffic conditions the data were collected (congested or free flow) [86]. [PERSON] et al. [71] used GPS probes and traffic-related information collected from social media to estimate urban traffic congestion more accurately. Additionally, they extract auxiliary information, including road congestion correlations, social events, road features, and points of interest. The results are evaluated on the real arterial network and show the effectiveness and efficiency of the proposed method. [PERSON] et al. [87] use the Deep Mixture Point process for event prediction in urban areas. This approach can use highly dimensional and multisource data to benefit from data in a rich urban context. [PERSON] [77] proposes a framework for data collection, filtering and fusion to get real-time traffic estimation \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Point Sensors**} & \multicolumn{3}{c}{**Point-to-**} & \multirow{2}{*}{**Area-Wide Sensors**} & \multirow{2}{*}{**Auxiliary Information**} \\  & & & & **Sensors** & & & & & \\ \hline [PERSON] et al. [69; 71; 79] & & & & X & X & X & X & X \\ \hline [PERSON] et al. [74] & X & & & X & & X & & & \\ \hline [PERSON] et al. [37], & & & & & & & & & \\ [PERSON] et al. [80], & & & & & & & & & \\ [PERSON] et al. [81], & & & & & & & & & \\ [PERSON] et al. [82], & X & & & X & & & & & \\ [PERSON] et al. [44], & & & & & & & & & \\ [PERSON] [83], & & & & & & & & & \\ [PERSON] et al. [72] & & & & & & & & & \\ \hline [PERSON] et al. [75] & X & & & & & & & & X \\ \hline [PERSON] et al. [36] & X & X & X & X & X & & & & \\ \hline [PERSON] et al. [84], & & & & & & X & X & & \\ [PERSON] et al. [46] & & & & & & X & & & \\ \hline [PERSON] et al. [85] & X & & & X & & X & & & \\ \hline [PERSON] et al. [70] & X & & & & X & X & & & \\ \hline [PERSON] et al. [78] & X & & & & & & & X \\ \hline \hline \end{tabular}
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Table 4: Multisource Data Used for Fusion and Congestion Estimation. and short-term travel time prediction. The framework for DF uses the Data Expansion Algorithm explained in [88], where the authors merged estimated traffic flows and used the proposed link-based on volume-delay function to assign travel times and average speed values to links. [PERSON] et al. [78] proposed a fusion of cellular network and traffic sensor data for route flow estimation. These two very different types of data are highly uncorrelated because cellular network data cannot be exactly mapped onto the road network. To overcome this lack of spatial information, caused by a rather sparse network of cell towers, authors introduced cellpaths, defined as a trajectory vehicle passes between two cell towers. Route flow prediction is finally achieved through convex optimization formulation using a map, cellpath flow, link flow, and O-D flow data.