## 2 Related Works
### Technology for Vehicle Idling Control
In detail, [PERSON] et al. (2020) explored the development of an eco-driving strategy that learns from driving data to generate optimal speed trajectories at signalized intersections by advising appropriate driving behaviors near intersections with the gaussian process regression (GPR) [38]. [PERSON] et al. (2019) studied vehicle emissions at unsaturated signalized intersections using a car-following model combined with a specific power emission model, finding that longer signal cycles, higher vehicle arrival rates, and increased traffic interference amplify emissions per vehicle [39]. [PERSON] et al. (2007) developed a microscopic traffic-emission simulation platform to investigate the relationship between traffic operations and emissions and found that vehicle emissions are strongly influenced by their operating modes, especially during acceleration, and that optimized signal timing plans can significantly reduce emissions for all vehicle categories [40]. [PERSON] et al. (2021) presented a study on the influence of signal timing on CO\({}_{2}\) emissions in a mixed traffic environment of fuel and electric vehicles by developing a signal timing model aimed at minimizing CO\({}_{2}\) emissions under different traffic conditions, resulting in the observation that optimizing signal timing from the perspective of CO\({}_{2}\) emissions is effective in balancing emissions during acceleration, deceleration, and idling stages [41]. [PERSON] et al. (2018) proposed an optimal velocity control and separation strategy for connected and automated vehicles (CAVs) to minimize idling at red lights by accessing traffic light timing and adjusting their speed accordingly; the simulation results showed that implementing the strategy resulted in a 19.2% reduction in total travel time [42]. [PERSON] et al. (2022) proposed a reinforcement learning (RL) approach to develop eco-driving control strategies at signalized intersections to reduce CO\({}_{2}\) emissions with connected autonomous vehicles (CAVs) investigated with the SUMO microscopic traffic simulator, resulting in significant reductions in CO\({}_{2}\) emissions up to 25% [43]. [PERSON] (2014) explored the use of vehicle-to-infrastructure (V2I) communication to improve mobility and reduce environmental impacts by combining a car-following model and an emission model to simulate vehicle behavior and calculate emissions at signalized intersections, showing that around 8% of CO\({}_{2}\) emissions can be saved [44]. Out of the analysis of related works on the effect of autonomous vehicles on the reduction of CO2 emission, little research has been conducted on the environmental benefits of automatic idling control function in autonomous vehicles with C-ITS in the urban network. Furthermore, unless the time it takes for an individual vehicle in the queue to start moving can be taken into account, the traffic signal information shared through V2I communication cannot be integrated into the proposed function in determining whether or not to control vehicle idling and in estimating the CO2 emission. Therefore, this study is distinguished from other studies in the respect that it considered the time to move in the queue based on the arrival time of shockwave from the stop line to vehicle position in the queue and compared it with the shared traffic signal information.