# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 5 Intersection State Estimation
### Data-Driven Methods  
The GPS is commonly used as a data source for data-driven methods. Required traffic data are mostly collected by GPS-equipped taxis or delivery vehicles. Since GPS cannot always provide accurate data, it is mostly used to determine conditions on the road level using additional preprocessing. Thus, speed profiles for very large road networks can be estimated and used in vehicle route optimization and traffic control [45]. In [54], turn-level congestion is detected by analyzing features of GPS data. By using clustering, the proposed method identifies spatial and temporal characteristics of congestion at the turn level, which is the main contribution of this paper.  
In [50], the authors developed an urban network-wide traffic state estimation method capable of processing high-resolution GPS data. The proposed method divides the observed area into cells with corresponding road segments and GPS records. Although the method performs well and can differentiate road segments when the cell is located at the intersection, the algorithm estimation accuracy is reduced in case of data loss during longer time periods. The approach described in [58] also uses GPS data to detect traffic states. The proposed method differentiates three traffic states: incident, slowed traffic and blocked traffic. Traffic states are detected based on vehicle speeds extracted from GPS data and are classified relative to speed thresholds. While this method does not require a learning process, the chosen speed thresholds can affect its performance. [PERSON] in [55] provides another illustration of how aggregated GPS data can be used to determine the queue length and the level of service at an intersection. These techniques rely on GPS data components, such as vehicle speed and position, which rely on the quantity of tracked cars and reliable GPS data samples. As it takes some time to gather adequate GPS data and process them, it is also crucial to consider the time delay brought on by data processing.  
Relying on GPS data, the authors of [51] proposed a method for traffic state estimation in an urban area on a city-wide scale. The proposed method represents data in the form of a Speed Transition Matrix (STM), where the transition is defined as a spatial change in vehicle trajectory when traveling between two consecutive road segments. The speeds that the vehicles had on the transition are calculated as harmonic mean speed, and it is written to the corresponding cell in the STM. After creating STMs, traffic state estimation is carried out based on the recorded data's center of mass characteristic. Obtained results indicated that the proposed method is suitable for traffic state estimation.  
Data-driven traffic state estimation methods rely on historical data, using statistical methods and machine learning techniques to determine real-time traffic states based on features found in historical data. Dependence on historical data has its drawbacks. Thus, such methods are prone to failure if an irregular event occurs or traffic trends change in the long term, especially in the case without having these two cases in the historical data [41]. Furthermore, low penetration of GPS-equipped vehicles leads to inaccuracy because of a lack of data. Additionally, this estimation method can also benefit from real-time traffic data sources directly into the estimation model, creating the foundation for the third approach described in continuation.