# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 7 Connected and Autonomous Vehicles Based Traffic Signal Control  
Traffic signals and vehicles are mutually dependent. Increased traffic demand needs different signal programs than decreased traffic demand to ensure maximum intersection throughput. Conventionally, research on TSC did not consider the characteristics of the traffic flow on the microscopic level (information about individual vehicles and their priorities). In reality, signal timing influences the movements of individual vehicles and, thus, their performances (such as emissions and fuel consumption). The most significant contribution to reducing Greenhouse Gas (GHG) emissions have eco-driving and platooning, while faster travel significantly contributes to the increase of GHG emissions [63]. Individual vehicle performances are the critical input to traffic control methods on how to adjust signal timing best [64]. The TSC system relies on the following control parameters when changing the signal program: green time, offset, change interval, and cycle length. TSC system balances these parameters among multiple intersections to reduce delays and the number of stops regarding the controlled small intersection network. Regarding current research, CAV-based TSC methods are usually designed first for isolated intersections, then extended to corridors and even intersection networks. However, the method extension is followed with the modification of the objective function to consider the coordination of all the intersections [64]. Common approaches to solving TSC problems are centralized and decentralized approaches and are applied in the case of mixed traffic flows also. Centralized approaches usually compress optimization problems of multiple \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Paper** & **Year** & **Data Source** & **Type** & **Applied Method** & **Impact** & **Benchmark** \\ \hline
[60] & 2019 & CVs, loop detectors & Model-driven method & [PERSON] car-following model-based CV signal control & Estimation vehicle position and speed & Intersection capacity utilization, EPICS \\
[62] & 2020 & Simulation, CV & Data-driven method & TSE algorithms for partially connected networks & Better overall performance compared to existing signal plan & Real-world Vissim simulation \\
[21] & 2018 & Simulation & Model-driven method & Two-lane cellular automation & Rued capacity growth rate is determined by CAV characteristics & Validation against real-world dataset \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tabular overview of referenced papers regarding the impact of mixed traffic flows on TSC. constraints to a single objective problem (e.g., the objective is to increase intersection throughput). Decentralized approaches assume that the neighboring intersections' traffic information is known as environmental input. Although distributed approaches can reduce computation complexity, those approaches may produce only locally optimal results. Good examples of centralized approaches are [65; 66]. In [65], the authors proposed adaptive coordination in the CV environment, which relies on data gathered from CV on-board units. Adaptive coordination incorporates dynamic programming and Mixed-integer Linear Programming (MILP). The proposed methodology uses Dynamic Programming (DP) to reduce vehicle delay at the intersection level for coordinated and uncoordinated phases. MILP is used to optimize offsets along the corridor. Although the method utilizes real-time data from CVs, in the case of CVs low penetration rate, the lack of data is compensated with the data from stop bar traffic detectors. Thus, on average flow data, corridor-level optimization was conducted offline to determine optimal cycle length. Similarly, in [66] the authors developed a signal timing optimization and coordination framework based on mixed-integer nonlinear programming.