在交通强国建设的大背景下，大力发展城市公共交通，推动城市可持续发展已然成为城市交通发展的必然要求。公交信号优先控制作为一种主动优先策略，可有效减少公交车辆在信号交叉口处产生的碳排放和延误，提升公交服务质量。为研究公交优先控制策略对交通碳排放的影响，基于交叉口车速随机特性，引入公交车速概率密度函数，分析了延误、停车次数、速度等主要参数对交通碳排放的影响。采用车速引导与绿时延长的组合控制策略，以交叉口上游路段以及交叉口范围内不同燃料类型的公交车和小汽车的碳排放减少量最优为上层目标，以人总延误减少量最优为下层目标，以引导速度以及非公交优先相位被压缩的绿灯时间为决策变量，建立单交叉口公交优先控制双层优化模型，并采用Gauss-Seidel迭代算法对模型进行求解。最后，将所建立的模型应用算例进行分析，结果表明，在引导提速与绿时延长策略下，交叉口整体的碳排放和人总延误减少量可分别达到25.63%和36.27%。模型有效降低了交叉口上游路段以及交叉口范围内的碳排放和人总延误，在推动可持续发展的同时，实现了交叉口整体通行效益最优。

In the context of the construction of a country with a strong transportation network, vigorously developing urban public transportation and promoting sustainable urban development has become an inevitable requirement for urban transportation development. Transit signal priority control, as an active priority strategy, can effectively reduce the carbon emissions and delays generated by buses at signal intersections, and improve the quality of bus service. A bus speed probability density function was introduced to study the effect of bus priority control strategy on traffic carbon emission, based on the speed stochastic characteristics of intersection. The effect of main parameters such as delay, stopping times, and speed on traffic carbon emission was analyzed. A bi-level optimization model of single-intersection bus priority control was established using the combination strategy of speed guidance and green extension. The model took the optimal carbon emission reduction of buses and cars with different fuel types in the upstream section of the intersection and the intersection control area as the upper-level objective, the optimal total people delay reduction as the lower-level objective, and the guidance speed as well as the compressed green time of the non-bus-priority phases as the decision variables. The Gauss-Seidel iterative algorithm was used to solve the model. Finally, the established model was applied to the calculation cases for analysis, and the results indicated that under the guidance acceleration and green extension strategy, the overall carbon emission and total passenger delay reduction of the intersection could reach 25.63% and 36.27%, respectively. The model effectively reduced carbon emissions and total passenger delays in the upstream sections of the intersection and the intersection control area, and optimized the overall traffic benefit of the intersection while promoting sustainable development.