# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 7 Connected and Autonomous Vehicles Based Traffic Signal Control
Problem complexity is reduced by decomposing the problem into two levels: intersection level, where DP is used to optimize phase duration, and corridor level, where the offsets of all intersection signal programs are optimized. The method was tested in a simulation environment under various scenarios, and obtained results revealed that the proposed method outperformed traditional actuated signal timing and coordination plan. Furthermore, while the signal coordination scheme was favorable for corridors with high traffic volumes, signal coordination had limited benefits for intersections with low traffic volumes. Distributed-Coordinated methodology for signal timing optimization in connected urban street networks is proposed in [67]. With the underlying assumption that all vehicles and intersections are connected and can share information, the network is decomposed into multiple individual intersections. For each intersection, a MILP needs to be solved. Such an approach is a real-time and scalable solution technique for signal timing optimization, where the timing decisions are made at the intersection level. Another distributed approach example is proposed in [68], where the authors used a back-pressure-based signal optimization method, combining fixed phase sequences with spatial model predictive control. Each intersection is controlled independently, while traffic queue lengths from surrounding intersections are used as input data. The main idea is to balance queue lengths among the intersections, thus, the traffic volume is evenly distributed over the traffic network. The results showed that even the distribution of queue lengths reduces travel time and increases network throughput. In [69], the authors based their research on the possibility of applying imaginary waiting queues and the back-pressure optimization method for intersection management. Imaginary queues are calculated periodically based on the traffic sensors' data that the intersection management system receives. The signal program for the traffic light is determined based on the calculated imaginary queues. The key difference is that the proposed method determines the phase schedule and duration based on historical data. Tabular overview of referenced researches in this section is summarized in Table 3. The above researches portray the application possibilities of CVs and CAVs in the area of TSC. The computing and communication capabilities of CAVs allow them to be decision-making agents in a multi-agent system. In this context, decision-making means that the CAVs cooperating with other CAVs in real-time can replace conventional TSC, making intersections autonomous. Implementation of autonomous intersections is enabled by infrastructure through V2X communication and CAVs decision-making possibility. Autonomous intersections are a complex topic that requires a lot of components in terms of communication, infrastructure, and security. A possible approach for the autonomous intersection implementation is vehicle platooning, where the vehicles are grouped, maintaining speed and distance. Platoons are coordinated with a leading vehicle, which negotiates the right of way on behalf of the following vehicles [70]. In [71], the authors researched platooning and proposed a scenario of a four-way autonomous intersection in a simulated environment. Applying the Platoon-based Delay Minimization function, results indicatedthat the average delay is reduced up to 40%. This research considered the ideal situation with CAVs and autonomous intersections. However, considering humans' imperfections, the question arises of how such a system would work with the HDVs. Research results indicated that the average delay increases for all vehicles if there is more than 5-10% of HDVs, where the CAVs were blocked by HDVs [72]. Nevertheless, CVs and CAVs can contribute to alleviating congestion on isolated intersections and corridor intersection configurations with their improved driving characteristics.