平面移动式立体车库待命位策略可以提高车辆出库效率，减少顾客等待时间，通过预测库内车辆出库时间节点，设计分区待命位策略。针对反向传播（back propagation, BP）神经网络存在样本依赖性较强的特点，提出一种K-近邻算法（k-nearest neighbor, KNN）-BP神经网络预测模型，以是否为工作日、工作日特殊时段（如早高峰、晚高峰），气温，降水4个方面作为特征向量，采用二进制集合转换构建各特征向量集，通过KNN对异常数据进行分组，提高BP神经网络预测精度，并基于预测时间建立出库概率分布，设定区域优先级参数以及RGV（rail guided vehicle）待命位策略。编写仿真程序，以西安小寨某商用立体车库运行数据进行验证，仿真结果表明：KNN-BP神经网络预测模型R²较传统BP神经网络提高了20.23%，设计待命位策略下较无待命位策略顾客平均等待时间减小35.82%，RGV平均服务时间降低39.51%，RGV运行能耗降低38.32%；较文献引用策略顾客平均等待时间减小14.18%，RGV平均服务时间降低13.29%，RGV运行能耗降低20.89%。研究成果为提高立体车库车运行效率提供参考。

The three-dimensional garage standby position strategy has been demonstrated to improve the efficiency of the vehicle out of the warehouse and reduce customer waiting times. A zone-based standby position strategy was designed by predicting vehicle departure time points within the parking system. To address the strong sample dependency of back propagation(BP) neural networks, a k-nearest neighbor(KNN) -BP neural network prediction model was proposed. Features such as workdays, specific time periods on workdays (e. g., morning and evening peak hours), temperature, and precipitation were utilized as feature vectors. These features were converted into binary sets to construct feature vector sets, and KNN was employed to group anomalous data, thereby enhancing the prediction accuracy of the BP neural network. Based on the predicted departure times, a departure probability distribution was established, and regional priority parameters along with an rail guided vehicle (RGV) standby position strategy were set. A simulation program was developed and validated using operational data from a commercial automated parking system in Xiaozhai, Xi'an. The simulation results demonstrate that the KNN-BP neural network predictive model increases by 20.17% compared to the traditional BP neural network. The average waiting time is reduced by 35.82%. The average service time of RGV is reduced by 39.51%, and the energy consumption of RGV operating is reduced by 38.32%. The average waiting time of the strategy cited strategy is reduced by 14.18%, the average service time of the RGV is 13.29%, and the energy consumption of RGV operating energy consumption is reduced 20.89%. The research results provide reference for improving the efficiency of stereo garage vehicles.