为了满足城市轨道交通乘客换乘接运公交的需求，有效减少因超过候车容忍时间而出现的客流流失量，研究考虑轨道交通列车到站时间、客流流失量的接运公交行车计划优化方法。以研究时段内乘客换乘需求和轨道交通到站时间等标明客流需求分布，以乘客到达接运公交首站的时间与公交发车时刻衔接关系、接运公交运能等描述换乘时间。考虑公交发车时间间隔、客流流失量、客流换乘需求等约束，在可调度的公交车辆数有限情况下，实现客流流失量、公交车辆数和乘客换乘候车总时间最小的多目标优化。由于优化目标之间存在矛盾，可利用带精英策略的非支配排序遗传算法（non-dominated sorting genetic algorithm II, NSGA-Ⅱ）求解模型，得到该问题的Pareto解集。最后，以实际接运公交线路进行算例分析，结果表明：优化模型兼顾公交运营成本和乘客换乘时间成本，可得到满足客流需求且代表不同侧重点的行车计划方案；接运公交车辆数相同时，本文方法相对于均匀发车方案可使乘客换乘候车总时间优化效果增加 8.0%；不均匀发车间隔下的平均满载率为59.3%，优于均匀发车间隔下的平均满载率50.2%。计算结果验证了模型和算法的有效性和合理性，有效提升接运公交和城市轨道交通时间和运能匹配度。

In order to meet the transfer and transportation requirements of passengers and to significantly mitigate the loss of passenger flow that exceeds the waiting tolerance threshold, the optimization method of feeder bus scheduling considering the arrival time and passenger flow loss of rail transit trains was studied. The distribution of passenger flow demand was characterized by the passenger transfer demand and the arrival times of rail transit within the study period. The transfer time was described by the alignment between the time passengers arrive at the station of the bus and the bus departure schedule, as well as the operational capacity of the buses. The constraints of bus departure intervals, passenger flow loss and transfer demands were considered, and the multi-objective optimization with the minimum passenger flow loss, bus number and passenger transfer waiting time was realized under the limited number of buses that can be scheduled. Due to the contradictions among the optimization objectives, the model was solved with Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-II). Finally, taking the actual bus routes as an example, the results show that the optimized model takes into account the bus operation cost and the passenger transfer time cost, and can obtain the scheduling that meets the passenger flow demand and represents different priorities. When the number of feeder buses is the same, the total transfer waiting time of the optimized method is reduced by 8.0% compared with the uniform headway. The average factor under the uneven is 59.3%, which is better than the average factor of 50.2% under the uniform. The calculation results validate the effectiveness and rationality of the model and algorithm, effectively enhancing the match between the time and capacity of buses and urban rail transit.