Volume 39 Issue 3
Jun.  2021
Turn off MathJax
Article Contents
ZHANG Yiming, CHEN Mingming, SHI Lei, KANG Ronggui. A Forecast of Short-term Passenger Flow of Rail Transit Based on IGWO-BP Algorithm[J]. Journal of Transport Information and Safety, 2021, 39(3): 85-92. doi: 10.3963/j.jssn.1674-4861.2021.03.011
Citation: ZHANG Yiming, CHEN Mingming, SHI Lei, KANG Ronggui. A Forecast of Short-term Passenger Flow of Rail Transit Based on IGWO-BP Algorithm[J]. Journal of Transport Information and Safety, 2021, 39(3): 85-92. doi: 10.3963/j.jssn.1674-4861.2021.03.011

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

doi: 10.3963/j.jssn.1674-4861.2021.03.011
  • Received Date: 2020-11-18
  • 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.

     

  • loading
  • [1]
    王明生. 城市轨道交通概论[M]. 北京: 人民交通出版社, 2012.

    WANG Mingsheng. Introduction to urban rail transit[M]. Beijing: China Communications Press, 2012. (in Chinese)
    [2]
    张亚运. 基于客流短时预测的城市轨道交通运营组织[D]. 西安: 长安大学, 2016.

    ZHANG Yayun. Operation organization of urban rail transit based on the passenger flow short-term prediction[D]. Xi'an: Chang'an University, 2016. (in Chinese)
    [3]
    ROOS J, GAVIN G, BONNEVAY S. A dynamic Bayesian network approach to forecast short-term urban rail passenger flows with incomplete data[J]. Transportation Research Procedia, 2017(26): 53-61. http://www.sciencedirect.com/science/article/pii/S2352146517308670
    [4]
    白伟华, 张传斌, 张塽旖, 等. 基于异常值识别卡尔曼滤波器的短期交通流预测[J]. 计算机应用研究, 2021, 38(3): 817-821. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ202103034.htm

    BAI Weihua, ZHANG Chuanbin, ZHANG Shuangyi, et al. Outlier-identified kalman filter for short-term traffic flow forecasting[J]. Application Research of Computers: 2021, 38(3): 817-821. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ202103034.htm
    [5]
    帅斌, 张玥, 张永超. 我国市郊铁路客流特征分析及需求预测[J]. 铁道工程学报, 2014(1): 20-23+106. doi: 10.3969/j.issn.1006-2106.2014.01.004

    SHUAI Bin, ZHANG Yue, ZHANG Yongchao. Analysis of suburban railway passenger flow characteristics and demand forecast in china[J]. Journal of Railway Engineering Society, 2014(1): 20-23+106. (in Chinese) doi: 10.3969/j.issn.1006-2106.2014.01.004
    [6]
    王兴川, 姚恩建, 刘莎莎. 基于AFC数据的大型活动期间城市轨道交通客流预测[J]. 北京交通大学学报, 2018, 42(1): 87-93. https://www.cnki.com.cn/Article/CJFDTOTAL-BFJT201801013.htm

    WANG Xingchuan, YAO Enjian, Liu Shasha. Urban rail transit passenger flow forecasting for large special event based on AFC data[J]. Journal of Beijing Jiaotong University, 2018, 42(1): 87-93. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BFJT201801013.htm
    [7]
    郭旷, 王雪梅, 张宁. 城市轨道交通短时客流不确定性预测模型[J]. 城市轨道交通研究, 2020, 23(1): 22-26. https://www.cnki.com.cn/Article/CJFDTOTAL-GDJT202001008.htm

    GUO Kuang, WANG Xuemei, ZHANG Ning. Uncertainty prediction model of short-term passenger flow in urban rail transit[J]. Urban Mass Tranit, 2020, 23(1): 22-26. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GDJT202001008.htm
    [8]
    SUN Yuxing, LENG Biao, GUAN Wei. A novel wavelet-SVM short-time passenger flow prediction in Beijing subway sys-tem[J]. Neurocomputing, 2015(166): 109-121. http://dl.acm.org/citation.cfm?id=2794209
    [9]
    赵阳阳, 夏亮, 江欣国. 基于经验模态分解与长短时记忆神经网络的短时地铁客流预测模型[J]. 交通运输工程学报, 2020, 20(4): 194-204. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202004020.htm

    ZHAO Yangyang, XIA Liang, JIANG Xinguo. Short-term metro passenger flow prediction based on EMD-LSTM[J]. Journal of Traffic and Transportation Engineering, 2020, 20(4): 194-204. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202004020.htm
    [10]
    HAO S, LEE D, ZHAO D. Sequence to sequence learning with attention mechanism for short-term passenger flow pediction in large-scale metro system[J]. Transportation Research Part C: Emerging Technologies, 2019(107): 287-300. http://www.sciencedirect.com/science/article/pii/S0968090X19300245
    [11]
    赵建立, 石敬诗, 孙秋霞, 等. 基于混合深度学习的地铁站进出客流量短时预测[J]. 交通运输系统工程与信息, 2020, 20(5): 128-134. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202005019.htm

    ZHAO Jianli, SHI Jingshi, SUN Qiuxia, et al. Short-time inflow and outflow prediction of metro stations based on hybrid deep learning[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(5): 128-134. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202005019.htm
    [12]
    PENG Yanni, XIANG Wanli. Short-term traffic volume prediction using GA-BP based on wavelet denoising and phase space reconstruction[J]. Physica A: Statistical Mechanics and its Applications, 2020(549): 123913. http://www.sciencedirect.com/science/article/pii/S0378437119321715
    [13]
    ZHOU Xingyu, LI Hongmei, ZHENG Weihao, et al. Short-term traffic flow prediction based on the IMM-BP-UKF model[J]. Journal of Highway and Transportation Research and Development(English Edition), 2019, 13(2): 56-64. doi: 10.1061/JHTRCQ.0000679
    [14]
    李建森, 沈齐, 范馨月. 城市道路短时交通流量预测[J]. 数学的实践与认识, 2019, 49(5): 192-197. https://www.cnki.com.cn/Article/CJFDTOTAL-SSJS201905021.htm

    LI Jiansen, SHEN Qi, FAN Xinyue. Forecast of short-term traffic flow on urban roads[J]. Mathematics in Practice and Theory, 2019, 49(5): 192-197. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SSJS201905021.htm
    [15]
    MIRJALILI S, MIRJALILI S M, LEWIS A. Grey Wolf Optimizer[J]. Advances in Engineering Software, 2014, 69(3): 46-61. http://www.sciencedirect.com/science/article/pii/s0965997813001853
    [16]
    傅蔚阳, 刘以安, 薛松. 基于灰狼算法与小波神经网络的目标威胁评估[J]. 浙江大学学报(工学版), 2018, 52(4): 680-686. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC201804010.htm

    FU Weiyang, LIU Yian, XUE Song. Target threat assessment using grey wolf optimization and wavelet neural network[J]. Journal of Zhejiang University(Engineering Science), 2018, 52(4): 680-686. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC201804010.htm
    [17]
    梁强升, 许心越, 刘利强. 面向数据驱动的城市轨道交通短时客流预测模型[J]. 中国铁道科学, 2020, 41(4): 153-162. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTK202004018.htm

    LIANG Qiangsheng, XU Xinyue, LIU Liqiang. Data-driven short-term passenger flow prediction model for urban rail transit[J]. China Rail Way Science, 2020, 41(4): 153-162. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTK202004018.htm
    [18]
    何正风. MATLAB R2015b神经网络技术[M]. 北京: 清华大学出版社, 2016.

    HE Zhengfeng. MATLAB R2015b neural network technology[M]. Beijing: Tsinghua University Press, 2016. (in Chinese)
    [19]
    NIU Peifeng, NIU Songpeng, LIU Nan, et al. The defect of the grey wolf optimization algorithm and its verification method[J]. Knowledge-Based Systems, 2019(171): 37-43. http://www.sciencedirect.com/science/article/pii/S0950705119300188
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)  / Tables(5)

    Article Metrics

    Article views (289) PDF downloads(12) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return