Queue Length Estimation and Optimal Vehicle Trajectory Planning Considering Queue Effects at Actuated Traffic Signal Controlled Intersections

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This research explores the implementation and evaluation of a green light optimal speed advisory (GLOSA) system in proximity to actuated traffic signals, considering the inherent uncertainty in switching times. Additionally, the impact of surrounding traffic is considered by optimizing vehicle trajectories with real-time queue estimation, derived from both loop-detector and probe vehicle data. Through comprehensive simulation experiments involving a single vehicle approaching an intersection with queued vehicles, as well as network-level simulations covering various market penetration levels (ranging from 0% to 100%), the fuel savings achieved by the GLOSA system is quantified. The results demonstrate substantial fuel savings of 31.4% and 35.4% when optimizing without and with consideration of the queueing process, respectively, from the perspective of individual vehicles. Furthermore, from a network-wide standpoint, a total fuel saving of 19.7% is observed for both scenarios. In addition, the optimization algorithm with considering the queuing process resulted in less average number of stops per vehicle than the case without considering it. Notably, the integration of the queue estimation yields remarkable improvements in system performance from the perspective of individual vehicles. However, it is found that considering the queue effects does not lead to an overall enhancement in system performance from a network-wide perspective. This research highlights the benefits and underscores the limitations of the GLOSA system when considering surrounding traffic. The incorporation of real-time queueing information for trajectory optimization offers valuable insights for the deployment and advancement of connected vehicle systems in real-world traffic environments.