# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
###### Abstract  
Mixed traffic flows are opening up new areas for research and are seen as key drivers in the field of data and services that will make roads safer and more environmentally friendly. Understanding the effects of Connected Vehicles (CVs) and Connected Autonomous Vehicles (CAVs), as one of the vehicle components of mixed traffic flows, will make it easier to avoid traffic congestion and contribute to the creation of innovative applications and solutions. It is notable that the literature related to the analysis of the impact of mixed traffic flows on traffic signal control in urban areas rarely considers mixed traffic flow containing CVs, CAVs, and Human Driven Vehicles (HDVs). Therefore, this paper provides an overview of the relevant research papers covering the topic of urban Traffic Signal Control (TSC) and mixed traffic flows. Best practices for intersection state estimation and TSC in the case of mixed traffic flows in an urban environment are summarized and possible approaches for utilizing CVs and CAVs as mobile sensors and actuators are discussed. mixed traffic flows; connected vehicles; connected autonomous vehicles; traffic signal control; intersection state estimation +
Footnote †: journal: _sciences_  
France, Germany, the United Kingdom, and the United States is expected to increase from USD 239.5 billion in 2020 to USD 293.1 billion by 2030. Thus, finding new ways to reduce traffic congestion is important for improving everyday life in urban areas. Due to the widespread use of numerous Traffic Signal Control (TSC) systems worldwide, TSC is now a key element of Intelligent Transportation Systems (ITS) traffic control services. Many new technologies can be adopted in TSC and especially ITS. Artificial neural networks, fuzzy systems, and evolutionary computation algorithms are the core components of computational intelligence, which provides flexibility, autonomy, and robustness to handle the non-linearity and randomness of traffic systems [4]. Accurate modeling and short-term traffic prediction are quite challenging due to traffic's intricate characteristics, stochastic, and dynamic traffic processes [5]. Due to their randomness and non-linearity, real-world data have limited applicability in short-term traffic state estimation [5; 6]. Being able to solve complex real-world problems, in recent years, multi-agent systems also gained significant importance in the field of traffic engineering [7]. As in any domain, solving traffic engineering problems also requires domain expertise. Specially since relying on multi-agent systems, problems can be divided into multiple smaller problems that require less domain expertise. Due to this advantage, it offers in resolving complex problems with uncertainties, multi-agent systems have drawn a lot of attention in the field of TSC. These systems provide a highly flexible and modular structure that incorporates domain expertise to achieve the optimal solution [7]. The emerging mixed traffic flows enable even more control strategies for TSC. Mixed traffic flows are the result of the coexistence of conventional Human-Driven Vehicles and Connected Vehicles (CVs). One has to note that a CV is considered any vehicle, e.g., Connected Autonomous Vehicles (CAVs), and HDVs, being able to communicate with the environment whether that is another vehicle, infrastructure, or pedestrians [8]. Having the possibility to operate as mobile sensors, the appearance of CVs and CAVs has opened up new areas of research regarding ITS. Equipped with Radio Detection and Ranging (RADAR), Light Detection and Ranging (LIDAR), cameras, and many other sensors, CVs have significant advantages compared to conventional fixed-mounted traffic sensors. While conventional sensors cover only specific measurement points, each CV is a mobile data source that can provide real-time spatiotemporal measurement data. Thus, instead of having the traffic information for certain road sections, the data from CVs or CAVs can provide insight into the traffic state along the road on a microscopic level.