基于IGWO-BP算法的轨道交通短时客流预测

轨道交通短时客流具有随机性和非线性的特点。为提高轨道交通短时客流预测结果的准确度，研究了基于改进的灰狼优化算法（IGWO）与BP神经网络的短时客流预测算法（IGWO-BP）。计算轨道交通客流不同时间序列的相关系数，确定了BP神经网络的输入和输出方式；用余弦思想和动态权重策略对原始灰狼优化算法改进，提高算法的全局搜索能力和寻优效率；用IGWO算法优化BP神经网络的初始权值和阈值，提高短时客流预测结果的准确性。预测了西安轨道交通2号线龙首原站周三早高峰15 min时间粒度的短时客流量，并将IGWO-BP算法的预测结果与其他5种模型（KF，GM，SVM，BPNN，GWO-BP）比较。结果表明，IGWO-BP算法的均方根误差为89.65，平均绝对百分比误差为1.16%，预测结果的精度和稳定性均为最优。 

A Forecast of Short-term Passenger Flow of Rail Transit Based on IGWO-BP Algorithm

Short-term passenger flow of rail transit has the characteristics of randomness and nonlinearity. An IGWO-BP algorithm is developed to forecast short-term passenger flow based on improved grey wolf optimization (IGWO) and BP neural network to improve the accuracy of predicting the short-term passenger flow of rail transit. The correlation coefficients of different time series of the rail-transit passenger flow are calculated to determine the input and output modes of the BP neural network. The cosine thought and dynamic weighting strategy are used to improve the orginal grey wolf optimization algorithm, thus enhancing the algorithm's global search and optimization. The IGWO algorithm is used to optimize the initial weights and thresholds of the BP neural network, which can improve the accuracy of predicting the short-term passenger flow. The work predicts the short-term passenger flow at the 15-min time granularity of the LONGSHOUYUAN Station of Xi'an Rail Transit Line 2 on Wednesday morning peak. The predicting results of the IGWO-BP algorithm are compared with those of the other five models (KF, GM, SVM, BPNN, and GWO-BP). For the IGWO-BP algorithm, the RMSE is 89.65, and the MAPE is 1.16%. The results show that the IGWO-BP algorithm has optimal accuracy and stability.