随着居民的出行需求增加，对公交的运营效率与服务质量有了更高的要求。但公交运营企业通常采用单线调度方式，易出现断面客流与运力投入不匹配，公交运营服务水平与资源利用效率不高的情况，亟需一种更加高效的调度优化方法。组合调度模式中多线路共用人车资源，有助于整合现有公交资源，提高运力供需匹配程度和公交运营效率。本文提出了基于客流特征的跨线调度线路组的识别规则和跨线车辆数的确定方法，以乘客出行成本和公交运营成本之和最小为优化目标，构建了加入跨线车辆的公交跨线组合调度优化模型，以发车类型和发车间隔进行编码，设计了改进遗传算法进行求解。研究选取北京市668路和122路的跨线组合为案例验证优化模型，引入乘客平均候车时间、线路满载率、运能匹配度、客流强度等指标，评估公交跨线组合调度优化的有效性。结果表明在公交跨线联合调度条件下，被支援公交线路的乘客平均候车时间缩短了11.8%，线路满载率减少了9.8%，运能匹配度和客流强度分别增加7.7%和8.7%，乘客出行成本与公交运营成本分别下降了15%和6%。

With the increase of residents’ travel demand, there are higher requirements for bus operation efficiency and service quality. But bus operation companies usually adopt single-line scheduling, which often leads to the mismatch between passenger flow and transport capacity input, low bus service level, and resource utilization inefficiency. A more efficient scheduling optimization method is urgently needed. In the combined scheduling mode, manpower and vehicles are shared among multiple lines, which helps to integrate the existing bus resources and improve the matching degree of supply and demand of transport capacity and bus operation efficiency. In this paper, the identification rules of the cross-line scheduling line group and the determination method of the number of cross-line vehicles were proposed based on the characteristics of passenger flow. The optimization goal was to minimize the sum of passenger travel costs and bus operating costs, and an optimization model of bus cross-line combined scheduling with cross-line vehicles was constructed. The departure type and departure interval was encoded and it was solved by an improved genetic algorithm. The lines No.668 and No.122 in Beijing were selected as the case of cross-line combination scheduling, and the average waiting time of passengers, line load factor, capacity matching degree, passenger flow intensity and other indicators were introduced to evaluate the effectiveness of the optimization model. The results show that, under the condition of bus cross-line scheduling, the average waiting time of passengers on the supported bus lines is shortened by 11.8%; the line load factor is reduced by 9.8%; the capacity matching degree and passenger flow intensity are increased by 7.7% and 8.7%, respectively; passenger travel costs and bus operating costs are reduced by 15% and 6%, respectively.