基于优化PSO-BP算法的耦合时空特征下地铁客流预测

为提高地铁客流预测的准确性，以西安地铁1号线为例，分析了地铁客流的耦合时空特征，提取了影响地铁客流变化的5个主要因素，包括节日、非节日、时间段、站点和天气，构建了反向传播(BP)神经网络，预测了地铁客流；利用引入自适应变异与均衡惯性权重的粒子群优化(PSO)算法，优化了BP神经网络，形成了考虑复杂因素影响的地铁客流预测系统；选取了换乘站、非换乘站的首站与中间站，引入天气、节日、非节日因素，对比了不同时间段下的BP神经网络模型，优化了PSO-BP神经网络模型的预测误差。研究结果表明：考虑天气、节日、非节日因素，换乘站点分时段优化PSO-BP神经网络模型预测的平均绝对误差、均方根误差和平均绝对百分比误差，较不分时段的优化PSO-BP神经网络模型分别平均下降了40.13%、31.46%和23.89%，较分时段的BP神经网络模型分别平均下降了17.50%、17.86%和17.32%；非换乘站点分时段优化PSO-BP神经网络模型预测的平均绝对误差、均方根误差和平均绝对百分比误差，较不分时段的优化PSO-BP神经网络模型分别平均下降了16.50%、20.99%和32.59%，较分时段的BP神经网络模型分别平均下降了11.48%、12.10%和17.73%；各站点分时段优化PSO-BP神经网络模型预测的平均绝对误差、均方根误差、平均绝对百分比误差，较不分时段的优化PSO-BP神经网络模型分别平均下降了24.37%、24.48%和29.69%，较分时段的BP神经网络模型分别平均下降了13.49%、14.02%和17.59%，因此，利用考虑多影响因素的优化PSO-BP神经网络模型能提高地铁客流预测的准确性。

Subway passenger flow prediction based on optimized PSO-BP algorithm with coupled spatial-temporal characteristics

To improve the accuracy of subway passenger flow prediction, by considering the Xi'an Metro Line 1 as an example, five main factors affecting subway passenger flow variations, such as festival, non-festival, time period, station, and weather, were extracted to analyze the coupled spatial-temporal characteristics of subway passenger flow. A back propagation (BP) neural network was constructed to predict the subway passenger flow. The proposed BP neural network was further optimized by using a particle swarm optimization (PSO) algorithm that introduced adaptive mutation and balanced inertia weights to form a subway passenger flow prediction system that could consider complex influence factors. Transfer stations and non-transfer stations including a first and an intermediate station were selected, the weather, festival, and non-festival factors were considered, and the BP neural network models for different time periods were compared. Then, the prediction errors of the PSO-BP neural network model were optimized. Research results show that by considering the weather, festival and non-festival factors, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the optimized PSO-BP neural network model predictions at transfer stations within the optimized time periods decrease by 40.13%, 31.46% and 23.89%, respectively, compared with the optimized PSO-BP neural network models prediction errors without the time periods, decrease by 17.50%, 17.86% and 17.32% compared with the BP neural network models prediction errors within the optimized time periods. The MAE, RMSE, and MAPE of the optimized PSO-BP neural network model predictions in the non-transfer stations within the optimized time periods decrease by 16.50%, 20.99% and 32.59%, respectively, compared with the optimized PSO-BP neural network model prediction errors without time periods, and decrease by 11.48%, 12.10% and 17.73%, respectively, compared with the BP neural network model prediction errors within the optimized time periods. The MAE, RMSE, and MAPE of the optimized PSO-BP neural network model predictions at each station within the optimized time periods decrease by 24.37%, 24.48% and 29.69%, respectively, compared with the optimized PSO-BP neural network model prediction errors without time periods, and decrease by 13.49%, 14.02% and 17.59%, respectively, compared with the BP neural network model prediction errors within the given time periods. Therefore, using the optimized PSO-BP neural network model and considering the influencing factors can improve the accuracy of subway passenger flow prediction. 8 tabs, 12 figs, 30 refs.