# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 4 Data Collection Technologies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technologies** & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Disadvantages**} \\ \hline Infrared sensors & - & multiple detection zone & - & spatial coverage is limited (depends on sensor type) \\  & - & small impact of & - & spatial coverage is limited (depends on sensor type) \\ \hline Magnetic sensors & - & not affected by & & \\  & weather conditions & & & \\  & - & can be used where loops & - & spatial coverage is limited \\  & & are not feasible (e.g., & & \\  & bridge decks) & & & \\ \hline Piezoelectric sensors & - & some models and & - & placed in groove along \\  & configurations provide & & roadway surface \\  & & weight in motion and speed & - & high costs for setting up \\  & & - & can provide travel time, & \\  & & O-D matrices & - & cannot provide volume and \\  & - & easy mounting & & vehicle count \\  & - & far greater privacy than ALPR & - & low detection accuracy \\  & - & low energy consumption & & \\ \hline Point-to-points sensors & - & easy mounting & & \\  & - & suitable for & - & cannot provide accurate \\  & & passenger detection & & basics traffic parameters \\  & - & low-cost components & & \\ \hline RFID detectors & - & low-cost components & - & cannot provide volume and \\  & - & high detection accuracy & & vehicle count \\  & & - & small detection zone \\ \hline ALP detectors & - & can provide volume, O-D matrices and travel time & - & privacy issue problematic \\  & - & high detection accuracy & & data protection \\ \hline  & & - & potential for & \\  & & real-time monitoring & & \\  & & large-scale spatial coverage & - & limited sample size and \\  & & - & location precision is & time-spatial coverage \\  & & high (10 m) & - & high equipment costs \\  & & - & cost-effective source of data & \\ \hline Area-wide sensors & & & - & for extract data sophisticated \\  & & - & no additional device is needed & - \\  & & large number of potential & - & location precision is low \\  & & probes cost-effective source & & (depends on used location \\  & & of data & & methods and size of mobile \\  & & of data & & network cells) \\  & & & - & limited and imprecise \\  & & & spatial coverage & \\ \hline Airborne imaginary & - & mobile multifunctional & - & limited recording time \\  & & detection device & - & affected by weather \\  & - & can provide density & - & conditions \\ \hline Social media data & - & cheapest data in terms of data & - & high costs \\  & & availability & & \\  & - & potential for real-time data & - & low reliability caused by \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Traffic information from only one data source is insufficient to meet the need of providing a real-time traffic congestion estimation in a large city [69]. For many ITS applications, the information provided by individual sensors is incomplete, inaccurate and/or unreliable [70]. Using multiple sensors for data collection helps in overcoming the disadvantages of some sensor technology and allows for a better assessment of the traffic states. Furthermore, multisource data can be useful for modeling people's travel trajectories and imply potential mobility patterns [70]. According to [71], the DF model can include people, web bots, or data fused at lower levels as data sources in addition to the sensors themselves. Table 4 summarizes the literature review with the aim to present the data sources used in DF for congestion estimation needs. [PERSON] et al. in [71] combined social media data and GNSS probe data to understand urban traffic congestion better. As an extension of this work in [69], the authors model the traffic congestion using GNSS probe data and social media data (Twitter). To model the traffic congestion more accurately, they also extracted rich auxiliary information such as social events, physical features of road, point of interest features, and weather information.