Complex Traffic Flow Model for Analysis and Optimization of Fuel Consumption and Emissions at Large Roundabouts

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.