The transportation sector is undergoing rapid transformation with the integration of artificial intelligence (AI), optimal driving strategies, and vehicle-to-everything (V2X) communication. While considerable research has been conducted on efficient driving and traffic management, most studies have treated these aspects independently.  Nevertheless, in this paper, we investigate the effects of both driving behavior and vehicle type (i.e., automated and human) on traffic management performances. The Gipps model is employed to represent human driving, and a typical adaptive cruise control (ACC) model is used for automated driving. Using the high-fidelity traffic simulation platform AIMSUN NEXT, we analyze congestion formation and intersection delays, focusing on how these vary across vehicle classes. Since traffic intersections constitute a significant portion of stop-and-go delays, we show how this delay, along with other major components, adds to the total amount of travel time wasted while sitting idle. After that, we analyze how this idling and drop in harmonic speed relate to vehicle type. As we show, human-driven vehicles behave quite differently from typical automated vehicles. It is apparent from our study that a fixed signaling phase is inefficient, and an adaptive signaling pattern is the best suited to different road scenarios and mixed traffic patterns. The findings indicate that fixed-time signal plans are suboptimal in mixed traffic conditions. In contrast, adaptive signal control can substantially improve intersection efficiency by leveraging both V2X technology and next generation 5G infrastructure to enable real-time adjustments to traffic signals based on predicted traffic patterns. Finally, we outline a predictive framework for future implementation in roadside units to enable proactive traffic management in mixed vehicular environments.