###### Abstract  
Reducing CO2 emissions is currently a key policy in most developed countries. In this article, we evaluate whether smart traffic lights can have a relevant role in reducing CO2 emissions in small cities, considering their specific traffic profiles. The research method is a quantitative modelling approach tested by computational simulation. We propose a novel microscopic traffic simulation framework, designed to simulate realistic vehicle kinematics and driver behaviour, and accurately estimate CO2 emissions. We also propose and evaluate a routing algorithm for smart traffic lights, specially designed to optimize CO2 emissions at intersections. The simulations reveal that deploying smart traffic lights at a single intersection can reduce CO2 emissions by 32% to 40% in the vicinity of the intersection, depending on the traffic density. The simulations show other advantages for drivers: an increase in average speed of 60% to 101% and a reduction in waiting time of 53% to 95%. These findings can be useful for city-level decision makers who wish to adopt smart technologies to improve traffic flows and reduce CO2 emissions. This work also demonstrates that the simulator can play an important role as a tool to study the impact of smart traffic lights and foster the improvement in smart routing algorithms to reduce CO2 emissions. CO2 emissions; smart traffic lights; smart intersections; CO2 estimation; reducing CO2; road traffic simulation; vehicle specific power; consumption estimation; intelligent traffic lights +
Footnote †: journal:  
0000-0001-6551-590X/0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00000-000000-000000-0000000000000000000000000000000000000  
One way to potentially reduce these CO\({}_{2}\) emissions is to replace passive traffic lights with smart systems, designed to optimise CO\({}_{2}\) emissions [19]. In the literature, which is reviewed in Section 2, there are numerous studies that describe and evaluate smart traffic routing strategies to improve traffic flows at intersections [20, 21, 22, 23, 24, 25, 26, 27, 28]. Some of these studies use vehicle to infrastructure (V2I) communications [29, 26] or vehicle to vehicle (V2V) communications [28]; while these approaches have a great potential to optimize traffic flows, they are currently impractical, due to the necessity of installing specific hardware/software in millions of old vehicles. Moreover, the optimization algorithms in these studies are generally focused on reducing waiting times and increasing average speed, while the reduction in CO\({}_{2}\) emissions comes as a consequence of better traffic flow. Also, the traffic patterns and vehicle behaviour are often oversimplified [18, 30], hence not representing real-life traffic patterns and emissions. Most studies conclude that smart traffic lights can improve traffic flows and reduce CO\({}_{2}\) emissions. However, it is not straightforward to transpose the results from these studies to our case study (a small city intersection), due to the differences in intersection types, different traffic characteristics, and the oversimplification of vehicle dynamics. Moreover, the driving style has a significant impact on emissions [31] but it is usually ignored or simplified in most studies. From this body of knowledge, a research gap has been identified: currently, there are no studies that simulate, with realistic driving patterns and accurate calculation of CO\({}_{2}\) emissions, the effects of feasible, emission-oriented smart routing strategies for intersections of small cities, considering their specific traffic profiles. Therefore, the main research question that this article addresses is as follows: considering the lower densities of traffic patterns in small cities, would a practicable emission-oriented smart algorithm be able to significantly reduce the CO\({}_{2}\) emissions at intersections?