Impact of driving behavior on carbon emissions at stop controlled intersections

Driver behavior significantly influences vehicle fuel consumption and exhaust emissions. Clarifying the relationship between inefficient driving habits (e.g., idling and rapid acceleration/deceleration) and vehicle carbon emissions is essential for urban carbon-reduction strategies. Using extensive, high-precision trajectory data, this study applies the vehicle specific power (VSP) method to calculate carbon-emission characteristics at various scales and constructs a comprehensive driving-behavior feature set. The distribution of vehicle carbon emissions is examined, while a <italic>t</italic>-distributed stochastic neighbor embedding (<italic>t</italic>-SNE) is employed for feature reduction, followed by <italic>k</italic>-medoids clustering to identify common patterns in high-emission driving behaviors. Moreover, the maximal information coefficient (MIC) method is applied to analyze the effects of driving behaviors under different road-priority conditions on carbon emissions. Results show that vehicles can be classified into three types, which are idling, normal and aggressive acceleration, accounting for 28.8%, 44.9% and 26.3%, respectively. Carbon emissions are strongly correlated with road priority, with significant differences in emission patterns between primary and secondary roads. For high-priority road segments, carbon emissions are strongly associated with acceleration-related features; for low-priority road segments, heading-angle variation, idling proportion, and minimum speed exhibit stronger correlations with emissions. By identifying the driving patterns associated with high carbon emissions, this study proposes targeted strategies to reduce carbon emissions under dynamic urban-traffic conditions, contributing to the improvement of urban carbon-reduction systems. This analysis highlights the importance of addressing driving habits in the development of effective carbon reduction policies in urban environments.