建立了基于 BP 神经网络的机场巴士行程时间可靠性预测模型，量化了机场巴士线网可靠性程度，并以可靠性最大化为目标，综合考虑时间、站点、服务等约束条件，构建了机场巴士线网优化模型． 然后采用爬山算法获取线路初始解，以可靠性建立适应度函数，采用不同变异率、交叉率设计混合遗传算法进行求解． 实例研究结果显示:高峰时段南京禄口机场巴士线网可靠性仅为 0． 62，城区内路段可靠性较城区外低约 15%，整体可靠性水平偏低;采用混合遗传算法的优化过程受交叉率、变异率影响大，较低的交叉率和较大的变异率会增加寻优过程的不稳定性;采用交叉率 0． 9、变异率 0． 05 的模型时目标函数值为 0． 79，可靠性水平较优化前提升了 11． 5%，优化效果显著． 该方法为优化机场巴士线网、提升机场对外交通服务效率提供了科学依据．

In order to quantify the reliability of airport bus line networks,a reliability prediction model of the air- port bus travel time is constructed based on the BP neural network.Next,an optimization model of the airport bus line network is constructed,the objective of which is to maximize the reliability,and such constraints as time,sites and services are taken into account.Then,the Hill-climbing algorithm is adopted to achieve the initial solutions for the lines,and a fitness function based on the reliability is established.Finally,a hybrid genetic algorithm with dif- ferent mutation and crossover rates is designed to solve the constructed optimization model.Case study results show that (1) during peak hours,the reliability of the bus line network at Nanjing Lukou International Airport is only 0. 62,the reliability of inner city roads is about 15% lower than that of outer city roads,and the overall reliability is at a low level; (2) the optimization process through the hybrid genetic algorithm is greatly affected by the cross- over and mutation rates,and lower crossover rate and higher mutation rate can increase the instability of the optimi- zation process; and (3) when the crossover rate is 0. 9 and the mutation rate is 0. 05，the objective function value of the constructed optimization model is 0. 79,and the reliability is 11. 5% higher than that before the optimization,which means that the optimization effect is significant.This method provides a scientific basis for the optimization of the bus line network of airports and the corresponding efficiency improvement of the external transport services.