城市轨道交通作为重要的公共交通方式，需在满足乘客出行需求的同时，降低能源消耗成本。为同时满足乘客出行效率与企业能源成本控制的双重目标，综合考虑列车发车间隔、载客定员及客流量等约束条件，构建了基于乘客需求与企业利益的列车运行优化模型。针对该优化模型，提出了一种卷积神经网络增强遗传算法。案例的计算结果表明卷积神经网络增强遗传算法比传统的遗传算法求解速度更快，且更不容易陷入局部最优。利用本文提出的模型和算法优化后的出行时间减少了10.21%，能耗降低了5.13%。研究结果为城市轨道交通时刻表优化提供了有效的理论依据，能够在降低企业能耗成本和减少乘客出行时间方面发挥作用，在提升交通运输系统效率和节约资源消耗方面具有重要意义。

As an important mode of public transportation, urban rail transit needs to meet passenger travel demand while reducing energy consumption costs. To meet the dual goals of passenger travel efficiency and enterprise energy cost control, the constraints of train departure interval, passenger capacity and passenger flow was considered, and a train operation optimization model was constructed based on passenger demand and enterprise interests. For this optimization model, a convolutional neural network enhanced genetic algorithm was proposed. The calculation results of the case show that the convolutional neural network enhanced genetic algorithm is faster than the traditional genetic algorithm, and it is less likely to fall into local optimal. Using the model and algorithm proposed, the travel time of optimized passengers is reduced by 10.21% and the energy consumption after optimization is reduced by 5.13%. The result provides an effective theoretical basis for the optimization of urban rail transit schedules, which can play a role in reducing the energy consumption cost of enterprises and reducing the travel time of passengers and has important significance in improving the efficiency of transportation system and saving resource consumption.