# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 4 Conventional Traffic Signal Control Strategies
### Adaptive Traffic Signal Control
#### 4.3.2 New Adaptive Traffic Signal Control Strategies  
Recent approaches to creating ATSC are based on machine learning [10]. Machine learning presents the foundation of intelligent traffic signal controllers with the ability to improve in the long term by gaining new knowledge on how to control traffic flows in different situations. The performance of such intelligent traffic signal controllers greatly depends on how they are trained and developed using optimization algorithms [35]. Recent years have seen significant progress in forecasting algorithms as well as the availability of real-time data, which has accelerated the development and applications of machine learning and artificial intelligence in TSC. Proactive models can predict the flow of traffic before it happens and calculate the necessary changes in the TSC system to prevent it completely or reduce the adverse effects [22].  
Whether real-time measured or predicted traffic volume values are used, these ATSC systems have their pros and cons. One aspect of their operation is generating a so-called \"green wave signal\" for large vehicle flow by optimizing the network's traffic signal offset values according to the current traffic demand. Generating a \"green wave signal\", ATSC strategies can reduce delay and the number of stops and consecutively increase the throughput of intersections. A reduced number of stops reduces fuel consumption, which positively impacts the environment in terms of air and noise pollution. Approximation models of energy consumption and gas emissions have been used as objective functions in traffic signal optimizations, and as a result, using such approximation models may result in an unrealistic traffic signal program configuration [36]. A signal program should be robust such that it is less sensitive to demand variations and can maintain near-optimal performance during varying traffic demand [37].  
ATSC systems use both predicted and real-time traffic arrivals to maximize the corresponding objective functions. Such systems also use cameras and inductive loop/magnetic detectors to track vehicles, which has considerable downsides in terms of cost and functionality. Namely, in urban areas, a green wave can be generated only in one-way corridors with ensured communication between intersection controllers. The system's initial implementation costs are around USD 30,000 per junction, or USD 28,800 per mile every year [38]. These systems also require a sizable communication network that can support centralized control with a high data rate. As a result, many towns throughout the world have created systems that are expensive to construct and run. Moreover, there is the risk of experiencing problematic issues with the entire system when the central control computer malfunctions.  
In [39], authors designed ATSC for arterial intersections based on Reinforcement Learning (RL). Its advantage is the ability to constantly learn during operation. Depending on the perception of the traffic dynamics, the RL agent dynamically controls offsets and green splits of the arterial intersections. The proposed algorithm is tested in simulation software in 200 random traffic scenarios. Compared to a fixed-time signal scheme with optimization of offset and green-split, results showed a reduction in the number of stops and delays. Such an approach enables improvement during operation. Thus, traffic control at signalized intersections should be based on adaptive approaches, rather than conventional and actuated signals [40].