# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 1 Introduction  
Greenhouse gas emissions pose a global challenge. According to the International Energy Agency (IEA) [1], the transport sector is responsible for 25-30% of a country's total carbon emissions. Within the transportation system, road transport contributes approximately 80% of emissions and energy consumption, with passenger cars responsible for 66.3% of road transport emissions, predominantly on urban roads. Given that exhaust emissions from passenger cars on urban roads are directly released into living areas, they have a significant impact on public health and travel experience, creating an urgent need for emission reduction. Signal-controlled intersections are fundamental components of urban roads and critical scenarios for urban road emission reduction in urban roads. When vehicles pass through signal-controlled intersections, some must come to a complete stop at red lights. During this process, vehicles undergo sequential conditions of deceleration, idling, and acceleration. Evaluations using microscopic simulation models show that the stopping conditions lead to additional emissions [2; 3]. Particularly during the start-up acceleration phase after a stop, vehicles emit a significant amount of pollutants [4; 5]. To address this issue, many studies [6; 7; 8; 9; 10; 11; 12] have developed adaptive signal control methods aimed at reducing vehicle stops as much as possible to improve carbon emissions at intersections. These methods optimize the allocation of green light durations at intersections based on the number of vehicles arriving from each direction. The number of arrival vehicles is predicted based on data collected from upstreamdetectors. However, these methods exhibit limitations in practical application due to their reliance on the assumption that all vehicles travel at a predefined speed. In reality, drivers cannot always achieve this predefined speed. Variations in driving behavior and individual differences result in fluctuations in actual vehicle speeds. For example, conservative drivers may drive slower than the predefined speed and miss the green light phase at intersections. In contrast, aggressive drivers may drive faster and arrive too early, resulting in additional stops and increased emissions. Indeed, the challenges faced by adaptive signal control methods stem from the uncertainty of vehicle arrival times. To overcome these limitations, it is critical to enhance the optimization of signal control methods by introducing vehicle speed guidance [13]. With the maturation of wireless communication, connected vehicles (CVs) have emerged. CVs can use Vehicle-to-Infrastructure (V2I) communication to transmit driver information to the signal control center. This information includes departure points, destinations, and precise trajectory data, such as per-second speed and position. Additionally, CVs can receive intersection signal plans in advance and adjust their speeds accordingly [14; 15; 16]. This reduces the impact of unpredictable vehicle arrivals on control systems, helping to avoid stops at intersections during red lights. Consequently, it further contributes to the reduction in carbon emissions [17; 18; 19; 20]. Numerous studies [21; 22; 23; 24; 25; 26; 27; 28; 29] have developed eco-friendly coordinated signal control with vehicle speed guidance. These methods provide second-by-second speed planning for vehicles, ensuring that vehicles arrive at intersections in alignment with green phases, maximizing the number of vehicles passing through and reducing stops. However, most of these studies assume that vehicles possess autonomous driving capabilities, enabling a precise execution of speed guidance commands. This assumption is unrealistic in the near future, as it is projected that Connected and Autonomous Vehicles (CAVs) will not achieve a 100% market penetration rate (MPR) by the 2060s [30]. Consequently, in the short term, the practical implementation of these methods remains challenging.