## 2 Literature Review  
Using smart traffic lights to improve road traffic flows at intersections has been extensively studied in the literature [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]. Most studies aim to optimize the delay and speed of traffic flows, but the impact on CO\({}_{2}\) emissions is not always thoroughly investigated. [PERSON] [30] proposed reinforcement learning algorithms to optimize traffic light control. Experimental results from the simulation showed that these algorithms reduced average waiting times by 25% in crowded traffic, compared to manually designed non-adaptive controllers. However, the traffic patterns used in the simulation were not realistic (all cars were equal and travelled at the same speed) and the impact on CO\({}_{2}\) emissions was not studied. In [26], the authors proposed an adaptive traffic signal system based on vehicle to infrastructure (V2I) communications, which optimizes the duration of the cycle's green phases dynamically. Results show that delay may be decreased by 28%, and CO\({}_{2}\) emissions may be 6.5% lower, when compared with the classical pretimed method. [PERSON] [29] assessed the potential of a Traffic-Light-to-Vehicle Communication (TLVC) application to reduce CO\({}_{2}\) emissions, with a focus on optimal gear shifting. A single-vehicle analysis presented significant gains in terms of emissions, but a wider road-network simulation showed a reduction in fuel consumption of only up to 8%. [PERSON] [19] evaluated the impact of Virtual Traffic Light (VTL), an \"infrastructureless\" traffic control system based solely on Vehicle-to-Vehicle (V2V) communication, and the simulation results show a reduction in CO\({}_{2}\) emissions reaching nearly 20% in high-density traffic conditions. [PERSON] [33] proposed an algorithm called oldest arrival first (OAF), which can reduce the delays under light and medium vehicular traffic loads, as compared with three other methods: a vehicle-actuated traffic signal controller, [PERSON]'s algorithm [34], and a fixed-time algorithm. However, under heavy vehicular traffic load, the gains of the OAF algorithm are marginal, and nothing is said about CO\({}_{2}\) emissions. [PERSON] [35] proposed an intelligent traffic light controlling (ITLC) algorithm, which considers the real-time traffic characteristics of the competing traffic flows to schedule the traffic light cycle. Moreover, an arterial traffic light (ATL) controlling algorithm is also proposed, which can coordinate intelligent traffic lights installed at each road intersection, to generate an efficient traffic schedule for the entire road network. Experimental results revealed that the ITLC algorithm increases the traffic flow by 30% compared with the online algorithm (OAF). The ATL can improve traffic flows in arterial streets by 70% more than the passive independent traffic light scheduling system, but the impact on CO\({}_{2}\) emissions is not investigated. [PERSON] [18] studied the CO\({}_{2}\) emissions at a busy intersection in the city of Celje, Slovenia. The conclusion was that most of the CO\({}_{2}\) is produced while waiting and in the accelerating phase in front of traffic lights; traversing the intersection without stopping uses significantly less fuel. The study also points out that the CO\({}_{2}\) emissions would significantly decrease in a hypothetical best-case scenario without any stops. However, the traffic model was simplified with several assumptions that makes it highly unrealistic. Moreover, the best-case scenario is not feasible, and the article therefore does not present a practical optimized scenario. In [36], an efficient traffic light scheduling algorithm (SmartLight) is described, which targets several typologies of road intersections. The performance evaluation of this algorithm demonstrates good performance and shows significant reduction in the average waiting delay time of traveling vehicles compared to the ITLC and ETLSA algorithms.