Intelligent transportation systems (ITSs) present new opportunities for enhanced traffic management by leveraging advanced driving behavior sensors and real-time information exchange via vehicle-based and cloud–vehicle communication technologies. Specifically, onboard sensors can effectively detect whether human-driven vehicles are adhering to traffic management directives. However, the formulation and validation of effective strategies for vehicle implementation rely on accurate driving behavior models and reliable model-based testing; in this paper, we focus on large roundabouts as the research scenario. To address this, we proposed the Three-Stage Cellular Automata (TSCA) model based on empirical observations, dividing the vehicle journey over roundabouts into three stages: entrance, following, and exit. Furthermore, four optimization strategies were developed based on empirical observations and simulation results, using the traffic efficiency, delay time, and dangerous interaction frequency as key evaluation indicators. Numerical tests reveal that dangerous interactions and delays primarily occurred when the roundabout Road Occupancy Rate ($\rho$) ranged from 0.12 to 0.24, during which times the vehicle speed also decreased rapidly. Among the strategies, the Path Selection Based on Road Occupancy Rate Recognition Strategy (Simulation 4) demonstrated the best overall performance, increasing the traffic efficiency by 15.65% while reducing the delay time, dangerous interactions, and frequency by 6.50%, 28.32%, and 38.03%, respectively. Additionally, the Entrance Facility Optimization Strategy (Simulation 1) reduced the delay time by 6.90%. While space-based optimization strategies had a more moderate overall impact, they significantly improved the local traffic efficiency at the roundabout by approximately 25.04%. Our findings hold significant practical value, particularly with the support of onboard sensors, which can effectively detect non-compliance and provide real-time warnings to guide drivers in adhering to the prescribed traffic management strategies.

Traffic emissions pose a substantial challenge for contemporary societies, particularly at roundabouts, where high levels of vehicle interaction and the associated emission dynamics are prevalent. Building upon this, a cellular automata model was developed to simulate traffic characteristics, including fuel consumption, emissions (CO, HC, and NO_x), and vehicle speed at a large roundabout. The model examines critical parameters, such as interaction, stop-and-go behavior, density, speed, and spacing, to identify the factors influencing fuel consumption and emissions in roundabout traffic. Numerical verification confirmed the model’s effectiveness in replicating complex traffic flows at large roundabouts, while also revealing that driving behavior, particularly during lane entry, is a critical factor influencing fuel consumption and emissions. Therefore, we proposed four optimization strategies—two space-based and two behavior-based—aimed at reducing emissions and enhancing traffic efficiency. Simulation results demonstrated that the behavior-based strategies achieved reductions of up to 18.40%, 43.20%, 28.98%, and 30.02% in fuel consumption and emissions, along with an 8.88% increase in traffic efficiency. In contrast, the space-based strategies improved traffic efficiency by 10.26%, while reducing fuel consumption and emissions by 8.25%, 32.64%, 18.48%, and 18.09%. While the space-based strategies enhanced traffic efficiency more, their overall optimization effects were relatively modest. Thus, integrating these strategies can enhance roundabout traffic efficiency across varying conditions, while reducing fuel consumption and emissions. These findings can enhance our understanding of the traffic parameters affecting vehicular emissions, offering crucial insights for urban planners and policymakers to optimize roundabout design and management toward greater sustainability and environmental benefits.