为了能够更加准确地实现地铁客流预测，提出了一种基于经验模态分解算法（empirical mode decomposition，EMD）优化非线性自回归（nonlinear auto regressive，NAR）动态神经网络的地铁客流量短时预测模型.分析地铁客流量数据后发现日客流量具有一定的变化规律，为此使用了基于时间序列的NAR动态神经网络，该网络具有优秀的非线性动态拟合能力和反馈记忆的功能.结合EMD经验模态分解算法优化NAR动态神经网络预测模型，以此来减少预测误差，提高预测精度.结果显示，EMD-NAR神经网络组合预测模型适用于地铁客流的短时预测，预测精度可达93%，具有较好的应用价值.
In order to forecast the subway passenger flow more accurately, a short-term prediction model of subway passenger flow based on empirical mode decomposition (EMD) optimization of nonlinear auto regressive (NAR) dynamic neural network is proposed. First, by analyzing subway passenger flow data, we find that the daily passenger flow performs in a certain change rule. Second, we select a time-based NAR dynamic neural network, which has excellent nonlinear dynamic fitting ability and feedback memory function. Furthermore, in order to reduce prediction errors and improve prediction accuracy, we use EMD empirical mode decomposition algorithm to optimize the NAR dynamic neural network prediction model. Simulation results show that the EMD-NAR neural network combined prediction model is well applicable for short-term prediction of subway passenger flow with high prediction result accuracy about 93%.
[1] 林培群, 陈丽甜, 雷永巍. 基于K-近邻模式匹配的地铁客流量短时预测[J]. 华南理工大学学报(自然科学版), 2018, 46(1):50-57. Lin P Q, Chen L T, Lei Y W. Short-term forecasting of subway traffic based on K-nearest neighbour pattern matching[J]. Journal of South China University of Technology (Natural Science Edition), 2018, 46(1):50-57. (in Chinese)
[2] 邹东, 刘琼, 黄梓荣. 城市轨道交通线网时空相关性客流预测研究[J]. 城市轨道交通研究, 2016, 19(3):32-37. Zou D, Liu Q, Huang Z R. Spatial-temporal correlation prediction for passenger flow in urban rail transit network[J]. Urban Mass Transit, 2016, 19(3):32-37. (in Chinese)
[3] 卢志义, 聂惟聪, 陈丽珍. 基于ARMA模型的城市轨道交通客流量预测[J]. 河南科学, 2018, 36(5):646-651. Lu Z Y, Nie W C, Chen L Z. Prediction of urban rail transport flow based on ARMA model[J]. Henan Science, 2018, 36(5):646-651. (in Chinese)
[4] 熊杰, 关伟, 孙宇星. 基于Kalman滤波的地铁换乘客流预测[J]. 北京交通大学学报, 2013, 37(3):112-116, 121. Xiong J, Guan W, Sun Y X. Metro transfer passenger forecasting based on Kalman filter[J]. Journal of Beijing Jiaotong University, 2013, 37(3):112-116, 121. (in Chinese)
[5] 王莹, 韩宝明, 张琦, 等. 基于SARIMA模型的北京地铁进站客流量预测[J]. 交通运输系统工程与信息, 2015, 15(6):205-211. Wang Y, Han B M, Zhang Q, et al. Forecasting of entering passenger flow volume in Beijing subway based on SARIMA model[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(6):205-211. (in Chinese)
[6] 马飞虎, 饶志强. 城市道路短时交通流预测方法研究[J]. 公路, 2017, 62(6):192-196. Ma F H, Rao Z Q. Research on short term traffic flow forecast method of urban road[J]. Highway, 2017, 62(6):192-196. (in Chinese)
[7] Moreno-Chaparro C, Rivas E, Salcedo-Lagos J, et al. State of the art of electricity demand forecasting based on wavelet analisys and a nonlinear autoregressive model NAR[C]//2012 Workshop on Engineering Applications, Bogota, Colombia, 2012:71-76.
[8] 杨绍清, 吴晓飞, 章新华, 等. NAR模型的Korenberg算法及其应用[J]. 系统仿真学报, 2001, 13(4):439-441. Yang S Q, Wu X F, Zhang X H, et al. Korenberg's algorithm for NAR model and its applications[J]. Journal of System Simulation, 2001, 13(4):439-441. (in Chinese)
[9] Giulia N, Francesco S, Linda S. Impact of EMD decomposition and random initialisation of weights in ANN hindcasting of daily stream flow series:an empirical examination[J]. Journal of Hydrology, 2011, 406(3):199-214.
[10] 王婷. EMD算法研究及其在信号去噪中的应用[D]. 哈尔滨:哈尔滨工程大学, 2010.
[11] 高波, 张钦宇, 梁永生, 等. 基于EMD及ARMA的自相似网络流量预测[J]. 通信学报, 2011, 32(4):47-56. Gao B, Zhang Q Y, Liang Y S, et al. Predicting self-similar networking traffic based on EMD and ARMA[J]. Journal on Communications, 2011, 32(4):47-56. (in Chinese)
[12] 郭雅楠. 基于EMD的BP神经网络在公交客流预测中的应用[D]. 天津:天津大学, 2016.
[13] 王文波, 费浦生, 羿旭明. 基于EMD与神经网络的中国股票市场预测[J]. 系统工程理论与实践, 2010, 30(6):1027-1033. Wang W B, Fei P S, Yi X M. Prediction of China stock market based on EMD and neural network[J]. Systems Engineering Theory & Practice, 2010, 30(6):1027-1033. (in Chinese)
[14] 袁鲁山. 基于NAR神经网络的车速预测及应用[D]. 大连:大连理工大学, 2016.
[15] 李析男, 王宁, 梅亚, 等. NAR神经网络的应用与检验——以城市居民生活需水定额为例[J]. 灌溉排水学报, 2017, 36(11):122-128. Li X N, Wang N, Mei Y, et al. Application and validation of nonlinear auto-regressive neural network model:taking water supply to residential area as an example[J]. Journal of Irrigation and Drainage, 2017, 36(11):122-128. (in Chinese)
