灰色变异粒子群算法在公交客流量预测中的应用

摘要/Abstract

摘要：

由于公交客流量是公交系统发展规划的基础依据，因此提高公交客流量预测的准确性有利于城市公交的发展。利用粒子群算法优化参数的良好性能和灰色预测法适合预测不确定因素影响系统的优势，提出用灰色变异粒子群组合预测模型来预测公交客流量，提高公交客流量预测精度，并通过实例对组合预测模型的预测精度和有效性进行了分析。结果表明，此组合预测模型的预测精度优于单一的灰色预测模型，也优于其他几种常用预测算法，能很好地预测公交客流量，为公交系统的决策规划提供了可靠的科学数据。

关键词: 灰色模型, 变异粒子群算法, 公交客流量, 预测

Abstract:

Because urban public transit volume is the fundamental basis for the development and planning of bus system, improving its prediction accuracy is beneficial to the development of urban public transport. By using the good performance of the particle swarm algorithm to optimize the parameters and the advantage of the grey prediction method for predicting uncertainty factors affecting the system, a grey mutation particle swarm combinational prediction model is proposed to predict the urban public transit volume and improve the prediction accuracy of the urban public transit volume. The prediction accuracy and effectiveness of the combinational forecast model are analyzed and verified. The results show that the accuracy of the combinational prediction model outperforms the single gray prediction model and some commonly used prediction algorithms, can predict the urban public transit volume well, and provides reliable scientific data for the decision-making and planning of the public transport system.

Key words: grey model;mutation particle swarm optimization;public transport passenger volume;prediction

米根锁,梁利,杨润霞. 灰色变异粒子群算法在公交客流量预测中的应用[J]. J4, 2015, 37(01): 104-110.

MI Gensuo,LIANG Li,YANG Runxia. Application of the grey mutation particle swarm algorithm
in urban public transport passenger volume prediction [J]. J4, 2015, 37(01): 104-110.

参考文献

［1］Zhang Chunhui, Song Rui, Sun Yang. The shortterm passenger flow prediction of bus stations based on kalman filtering［J］.Journal of Transportation Systems Engineering and InformationTechnology,2011,11(4):154159.(in Chinese)
［2］Yan Qisheng, Wang Shitong.Support vector regression model of highway traveling passenger volume traffic forecasting［J］. Computer Engineering and Applications,2011,47(9):233235.(in Chinese)
［3］Hong Bingrong, Jin Feihu,Gao Qingji. Multilayer feedforward neural network based on ant colony system ［J］.Journal of Harbin Institute of Technology,2003,35(7):823825.(in Chinese)
［4］Zhang Cheng,Zhou Xiangfeng.Prediction of railway freight volumes based on grey forcastmarkov chainqualitative analysis［J］. Journal of Railway, 2007,29(5):1521.(in Chinese)
［5］Fang Lijun,Wu Zhong. The application of GM(1,3) model in highway passenger capacity forcast of transportion system ［J］.Journal of Highway and Transportion Research and Development,2006,23(3):163166.(in Chinese)
［6］Zhang Dahai, Wang Shifang，Shi kaiquan. Theoretical defect of grey prediction formula and its improvement ［J］.Systems EngineeringTheory& Practice, 2002,22(8):13.(in Chinese)
［7］Rui Liu,Li Xiaolei,Liu Sifeng.Deformation monitoring and forcasting model study based on grey system theory［J］. Advances in Intelligent and Soft Computing, 2009,62:555564.
［8］Zhao Zhigang,Chang Cheng. Adaptive particle swarm optimization algorithm with hybrid mutation operator［J］. Computer Engineering and Application,2011,47(17):4244.(in Chinese)
［9］Deng Julong. Grey theory ［M］.Wuhan:Huazhong University of Science and Technology Press,2002.(in Chinese)
［10］Li Song, Liu Lijun,Liu Yingpeng. Prediction for chaotic time series of optimized BP neural network Based on modified PSO［J］. Computer Engineering and Applications,2013,49(6):245246.(in Chinese)
［11］Zhang Feilian, Shi Feng. Stochastic gray system model for forcasting passenger and freight railway volume［J］. Journal of Central South University, 2005,36(1):158159.(in Chinese)
［12］Zhang Yan, Wang Fanzhen, Chen zengqiang,et al. Recurrent neural networksbased multivariable system predictive control［J］. Journal of Nankai University, 2006,39(1):4953.(in Chinese)
［13］Wang Qingrong, Zhang Qiuyu. Forecasting of shortterms urban public transit volume based on random gray ant colony neural network［J］. Application Research of Computers,2012,29(6):20802084.(in Chinese)
中文参考文献：
［1］张春辉,宋瑞,孙杨.基于卡尔曼滤波的公交站点短时客流预测［J］.交通运输系统工程与信息,2011,11(4):154159.
［2］颜七笙,王士同.公路旅游客流量预测的支持向量回归模型［J］.计算机工程与应用,2011,47(9):233235.
［3］洪炳熔,金飞虎,高庆吉.基于蚁群算法的多层前馈神经网络［J］.哈尔滨工业大学学报,2003,35(7):823825.
［4］张诚,周湘峰.基于灰色预测马尔可夫链定性分析的铁路货运量预测［J］.铁道学报,2007,29(5):1521.
［5］方丽君,吴中.GM(1,3)模型在交通系统公路客运量预测中的应用［J］.公路交通技术,2006,23(3):163166.
［6］张大海,汪世芳,史开泉.灰色预测公式的理论缺陷及改进［J］.系统工程理论与实践,2002,22 (8):13.
［8］赵志刚,常成.带变异算子的自适应粒子群优化算法［J］.计算机工程与应用,2011,47(17):4244.
［9］邓聚龙.灰理论基础［M］.武汉:华中科技大学出版社,2002.
［10］李松,刘力军,刘颖鹏.改进PSO优化BP神经网络的混沌时间序列预测［J］.计算机工程与应用,2013,49(6):245246.
［11］张飞涟,史峰.铁路客货运量预测的随机灰系统模型［J］.中南大学学报,2005,36(1):159159.
［12］张燕,王繁珍,陈增强,等.基于递归神经网络的多变量系统预测控制［J］.南开大学学报,2006,39(1):4953.
［13］王庆荣,张秋余.基于随机灰色蚁群神经网络的近期公交客流预测［J］.计算机应用研究,2012,29(06):20802084.

[1]	曹健, 陈怡梅, 李海生, 蔡强, . 基于图神经网络的行人轨迹预测研究综述[J]. 计算机工程与科学, 2023, 45(06): 1040-1053.
[2]	余泽鹏, 安业腾, 张烁, 杨自兴, 陆继翔, 曹蓉蓉, 陈轶洲, 李文中, 陆桑璐. 基于会话嵌入的应用程序使用预测[J]. 计算机工程与科学, 2023, 45(06): 1079-1086.
[3]	唐宇, 代琪, 杨志伟, 杨爱民, 陈丽芳, . 基于优化随机森林的软件缺陷预测算法研究[J]. 计算机工程与科学, 2023, 45(05): 830-839.
[4]	付接递, 李振东, 郭辉. 基于教与学和逐维柯西变异的鲸鱼优化算法[J]. 计算机工程与科学, 2023, 45(05): 940-950.
[5]	胡晨莹, 赵琰. 基于预测多对非对称直方图的可逆信息隐藏[J]. 计算机工程与科学, 2023, 45(03): 454-461.
[6]	申晓宁, 许笛, 宋丽妍, 姚铖滨, 王玉芳, . 采用离散烟花算法的移动群智感知异构任务分配[J]. 计算机工程与科学, 2023, 45(02): 321-331.
[7]	王雨露, 李飞, 杨震, 黄山, 张罡, 詹曙, . 基于深度前馈神经网络的多因子人体表面积计算模型[J]. 计算机工程与科学, 2023, 45(01): 119-126.
[8]	崔馨月, 蒋林, 杨坤, 惠超, 胡传瞻, 赵静. 一种HEVC帧内预测算法的动态自重构实现方法[J]. 计算机工程与科学, 2022, 44(12): 2120-2127.
[9]	王堃, 郑晨, 张立中, 陈志刚. 一种基于SARIMA-LSTM模型的电网主机负载预测方法[J]. 计算机工程与科学, 2022, 44(11): 2064-2070.
[10]	谢俊标, 江峰, 杜军威, 赵军. 基于改进人工鱼群算法与RBF神经网络的股票价格预测[J]. 计算机工程与科学, 2022, 44(11): 2080-2090.
[11]	胡艳芳, 熊文, 高炜. 基于 Spark 平台的网络游戏用户流失预测方法[J]. 计算机工程与科学, 2022, 44(10): 1730-1737.
[12]	唐阳坤, 鲜港, 杨文祥, 喻杰, 张晓蓉, 王耀彬. 基于用户行为的超级计算机作业失败预测方法[J]. 计算机工程与科学, 2022, 44(10): 1753-1761.
[13]	马满福, 姜璐娟, 李勇, 张强, 范颜军, 邓晓飞. 基于AFP的有向加权注意力流网络链路预测[J]. 计算机工程与科学, 2022, 44(10): 1762-1770.
[14]	朱正东, 吴寅超, 胡亚红, 蒋家强. 基于LSTM的集群用户作业执行时间预测模型[J]. 计算机工程与科学, 2022, 44(08): 1331-1341.
[15]	王林, 王燕丽, 安泽远. 改进粒子群算法优化回声状态网络的电力需求预测研究[J]. 计算机工程与科学, 2022, 44(08): 1457-1466.