# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 5 Intersection State Estimation
### Model-Driven Methods  
Model-driven methods are based on knowledge of physical traffic flows where applied models represent the physical flow. Models have high explanatory characteristics, which means it is possible to explain the inaccuracy even if the estimation is inaccurate. However, a poorly calibrated model can affect the performance of the estimation method [41]. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Paper** & **Year** & **Data Source** & **Type** & **Applied Method** & **Impact** & **Benchmark** \\ \hline
[42] & 2022 & Simulation, & Streaming-data & Bottleneck Probability & Bottleneck probability & Evaluated on four different simulated \\  & & FCD & driven method & Estimation Using & simulated motorway & motorway traffic \\  & & & & STM & traffic scenarios & scenarios \\
[43] & 2021 & Simulation, & Streaming-data & Intersection state & Intersection Traffic State & Crossvalidation \\  & & FCD & driven method & and Fuzzy logic & Estimation using STM & \\
[44] & 2022 & Simulation, & Streaming-data & Statistical analysis & Analysis of impact CVs & Statistical validation \\  & & & driven method & on STM accuracy & Estimation of & Real travel times, \\  & & & & congestion zones and & historical dataset, and \\
[45] & 2021 & GPS, FCD & Data-driven method & Data mining & time-varying travel time & state-of-the-art \\  & & & & & indicates & method \\
[46] & 2021 & FCD & Model-driven method & Directed graph & Estimation of vehicle & \\  & & & & density in every road & Realistic simulation \\
[47] & 2021 & Camera & Streaming-data & CNN & Robust approach for & Comparative analysis \\  & & driven method & & queue length estimation & to Yolo v3,4,5 \\
[48] & 2021 & GPS & Data-driven method & Clustering & identification on global & Real data \\  & & & scale & scale & compared to \\
[49] & 2020 & Street view & Model-driven method & GCN model & Model based prediction & Compared to Taxi \\  & & & & of congestion & GPS dataset \\  & & & & Schatten p-norm & Monitor and visualize & \\
[50] & 2020 & GPS & Data-driven method & model for & traffic dynamics via & kNN, NMF \\  & & & speed-matrix & stockastic congestion & \\  & & & completion & maps & \\
[51] & 2020 & GPS & Data-driven method & Classification, STM & Traffic state estimation & Cross-validation \\  & & & & on citywide scale & \\
[52] & 2019 & Camera & Streaming-data & Background & Fast video-based queue & Compared to \\  & & & & AdaBoost classifier & length detection & Adaboost-based method \\  & & & Adaptive & & \\
[53] & 2019 & GPS & Model-driven & multi-kernel support & Short-term traffic flow & Real data \\  & & & vector machine & prediction & \\
[54] & 2019 & GPS & Data-driven method & Classification & Turn-level congestion & Cross-validation with \\  & & & & Method for queue & labeled data \\
[55] & 2018 & GPS & Data-driven method & Data mining & length, level of service & \\  & & & & and control delay & \\
[56] & 2018 & Sensors, & Streaming-data & & & \\  & & data & driven method & & Robust traffic state & \\  & & & driven method & & estimation approach & Realistic simulation \\  & & & & & Historical data-based \\  & & & & model, moving \\  & & & & & average data  
The authors of [48] modeled the traffic flow network based on taxi's Global Positioning System (GPS) trajectories. The proposed model can reflect the real state of the network, where the nodes represent congested areas and the weight of the edges between nodes represent the congestion coefficient. Scalability is the main benefit of this approach, which is achieved by adjusting the size of the area represented by the node. Although the model was reliable, relying on the GPS traces introduced certain limitations regarding the precision of calculated distances and waiting times. In [57], a short-term traffic prediction method is proposed based on a novel single-time-step prediction model that simultaneously considers spatial and temporal characteristics of the traffic.