# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 5 Intersection State Estimation
### Streaming Data-Driven Methods  
In contrast to previously mentioned methods, streaming-data-driven methods do not require historical data, which makes them robust to unpredictable events. They rely on streaming data and weak assumptions without capabilities for future prediction. For accurate estimation, these methods require large amounts of (real-time) streaming data [41]. The mentioned STM-based method combined with Fuzzy-based systems used for intersection traffic state estimation is also applied in [43]. The road network is divided into segments of equal lengths, and CV speeds were collected to create STMs for each transition. Each STM is normalized as the input for the proposed fuzzy logic-based decision system. Based on the center of mass characteristic and applied set of IF-THEN rules, the fuzzy logic gives as output the bottleneck probability ranging from 0 to 1. The method is tested using a simulated environment containing an isolated intersection. Obtained results revealed the congestion's spatial and temporal characteristics, indicating that this method can be used for intersection traffic state estimation. Another application of STMs is described in [42] where it is used for bottleneck probability estimation on motorways. The proposed method computes vehicle speeds on consecutive motorway segments, and speeds are represented with STMs. Evaluation of the proposed method on different scenarios resulted in total accuracy of 92%, which indicates possible application on motorways with high CV penetration rates where CVs are used as mobile sensors. Thus, in [44] various CV penetration rates were tested to analyze STM accuracy. The results indicated that STM accuracy decreases during morning and afternoon rush hours. Furthermore, results also indicated that adding new CVs has much more impact on penetration rates up to 30% compared to penetration rates above 30%. To estimate the queue tail location, the approach defined in [56] fuses traffic detector data with the location and speed of the CVs in a mixed traffic flow containing conventional HDVs and CVs. To compensate lack of data at low penetration rates, the authors developed a probability-based approach making the proposed method robust to varying penetration rates of CVs. The results showed that the proposed method requires only one spot detector upstream of the link. In [46], the authors also relied on data fusion from different sources. The proposed method for traffic state estimation on urban networks utilizes data from three data sources: stationary flow sensors, turning ratios, and floating car data. The benefit of the proposed method is that it requires only turning ratios as an input, thus, road outflows modeling is avoided. This benefit can also be the main drawback since the method relies on turning ratios estimated from stationary flow sensors. Streaming data-driven methods also include methods based on the data obtained from various sensors for collecting motion information, such as transport video detectors, microwave radars, infrared sensors, ultrasonic sensors, passive acoustic sensors, and others [59]. The authors of [47] developed a video-based vehicle queue length estimation method. The proposed method detects vehicles as objects, and queue length is calculated as the number of detected objects multiplied by the average vehicle length. Similarly, in [52], the authors proposed a real-time video-based vehicle counting method for urban roads. The proposed vehicle counting method is used for each lane depending on the traffic congestion degree determined by the foreground area ratio. A quick area-based method for calculating the number of vehicles is used when the traffic is deemed to be congested. Otherwise, an approach that uses background difference and an Adaboost classifier to recognize and count vehicles quickly is used. The proposed video-based methods were robust to heavy traffic with accurate vehicle counts compared to ground truth data.