Volume 42 Issue 2
Apr.  2024
Turn off MathJax
Article Contents
Article Navigation >  Journal of Transport Information and Safety  > 2024  >  42(2): 158-165
CHEN Xiqun, SHEN Loutao, LI Junyi, LI Chuanjia. A Short-term Prediction for OD Passenger Flow in Urban Rail Transit Based on Heterogeneous Data Feature Extraction[J]. Journal of Transport Information and Safety, 2024, 42(2): 158-165. doi: 10.3963/j.jssn.1674-4861.2024.02.016
Citation: CHEN Xiqun, SHEN Loutao, LI Junyi, LI Chuanjia. A Short-term Prediction for OD Passenger Flow in Urban Rail Transit Based on Heterogeneous Data Feature Extraction[J]. Journal of Transport Information and Safety, 2024, 42(2): 158-165. doi: 10.3963/j.jssn.1674-4861.2024.02.016
shu

A Short-term Prediction for OD Passenger Flow in Urban Rail Transit Based on Heterogeneous Data Feature Extraction

doi: 10.3963/j.jssn.1674-4861.2024.02.016
  • 1.

    Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China

  • 2.

    Polytechnic Institute, Zhejiang University, Hangzhou 310015, China

  • Received Date: 2023-05-29
    Available Online: 2024-09-14
    Abstract
  • As an important basis for rail transit operations and travel choices, prediction for origin-destination (OD) passenger flow in urban rail transit is of great significance in intelligent transportation systems. The conventional convolutional neural network (CNN) mostly focuses on local OD features due to their translation invariance and local sensitivity. To improve its global perception capacity in OD matrix modeling, a heterogeneous data feature extraction machine (HDFEM) model is proposed based on attention mechanism. The model constructs a heterogeneous data OD spatio-temporal tensor and a geographic information tensor from the perspective of spatio-temporal characteristics and land use attributes. It segments and encodes heterogeneous data tensors via a tensor coding layer to obtain the features of tensor blocks in heterogeneous data tensors. Then, it connects the features of each tensor block through the attention mechanism to extract the spatial correlation among various OD matrix parts. This approach not only realizes multi-source heterogeneous data fusion, but also extracts remote features of OD matrix. Meanwhile, the model uses long short-term memory (LSTM) network to deal with the OD temporal feature. Compared with the convolutional neural network-based prediction model, the results on the Hangzhou metro auto fare collection (AFC) dataset show that the mean square error, mean absolute error, and normalized root mean square error of the HDFEM model decreases by 4.1%, 2.5%, and 2%, respectively. The importance of extracting whole spatial features for OD passenger flow prediction of urban rail transit is verified.

     

    • FullText(HTML)
  • loading
    • References(22)
  • [1]
    张艺铭, 陈明明, 石磊, 等. 基于IGWO-BP算法的轨道交通短时客流预测[J]. 交通信息与安全, 2021, 39(3): 85-92. doi: 10.3963/j.jssn.1674-4861.2021.03.011

    ZHANG Y M, CHEN M M, SHI L, et al. 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. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.03.011
    [2]
    蔡昌俊, 姚恩建, 王梅英, 等. 基于乘积ARIMA模型的城市轨道交通进出站客流量预测[J]. 北京交通大学学报, 2014, 38(2): 135-140.

    CAI C J, YAO E J, WANG M Y, et al. Prediction of urban railway station's entrance and exit passenger flow based on multiply ARIMA model[J]. Journal of Beijing Jiaotong University, 2014, 38(2): 135-140. (in Chinese)
    [3]
    谢臻, 郭建媛, 秦勇. 基于支持向量回归的地铁进站客流短时预测模型[J]. 都市快轨交通, 2020, 33(2): 82-86. doi: 10.3969/j.issn.1672-6073.2020.02.014

    XIE Z, GUO J Y, QIN Y. Short-term prediction model of subway entry passenger flow based on support vector regression[J]. Urban Rapid RailTransit, 2020, 33(2): 82-86. (in Chinese) doi: 10.3969/j.issn.1672-6073.2020.02.014
    [4]
    帅春燕, 谢亚威, 单君, 等. 基于SSA-SVR模型的城市轨道交通短时进站客流预测[J]. 都市快轨交通, 2022, 35(5): 76-83.

    SHUAI C Y, XIE Y W, SHAN J, et al. Prediction of short-term inbound passenger flow of urban rail transit based on the singular spectrum analysis and support vector regression model[J]. Urban Rapid Rail Transit, 2022, 35(5): 76-83. (in Chinese)
    [5]
    YANG X, XUE Q, DING M, et al. Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data[J]. International Journal of Production Economics, 2021, 231: 107920. doi: 10.1016/j.ijpe.2020.107920
    [6]
    崔洪涛, 陈晓旭, 杨超, 等. 基于深度长短期记忆网络的地铁进站客流预测[J]. 城市轨道交通研究, 2019, 22(9): 41-45.

    CUI H T, CHEN X X, YANG C, et al. Forecast of subway inbound passenger flow based on DLSTM recurrent network[J]. Urban Mass Transit, 2019, 22(9): 41-45. (in Chinese)
    [7]
    施俊庆, 李睿, 程明慧, 等. 基于动态时空神经网络模型的地铁客流预测[J]. 交通运输系统工程与信息, 2023, 23(2): 139-147.

    SHI J Q, LI R, CHENG M H, et al. Metro passenger flow prediction based on dynamic spatio-temporal neural network model[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(2): 139-147. (in Chinese)
    [8]
    HAO S, LEE D H, ZHAO D. Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system[J]. Transportation Research Part C: Emerging Technologies, 2019, 107: 287-300. doi: 10.1016/j.trc.2019.08.005
    [9]
    LIU Y, LIU Z, JIA R. DeepPF: a deep learning based architecture for metro passenger flow prediction[J]. Transportation Research Part C: Emerging Technologies, 2019, 101: 18-34. doi: 10.1016/j.trc.2019.01.027
    [10]
    DJUKIC T, VAN LINT J W C, HOOGENDOORN S P. Application of principal component analysis to predict dynamic origin-destination matrices[J]. Transportation Research Record, 2012, 2283(1): 81-89. doi: 10.3141/2283-09
    [11]
    BASAGOITI R, BEAMURGIA M, PETERS R, et al. Origin destination matrix estimation and prediction in vertical transportation[C]. 2nd Symposium on Lift and Escalator Technologies, Northampton, UK: LESET, 2012.
    [12]
    LEE C Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. doi: 10.1109/5.726791
    [13]
    徐新颖. 基于卷积神经网络的城市轨道交通短时客流预测方法研究[D]. 福州: 福州大学, 2023.

    XU X Y. Research on short-term passenger flow forecasting method of urban rail transit based on convolutional neural network[D]. Fuzhou: Fuzhou University, 2023.
    [14]
    王博文, 王景升, 王统一, 等. 基于卷积神经网络与门控循环单元的交通流预测模型[J]. 重庆大学学报, 2023, 46(8): 132-140.

    WANG B W, WANG J S, WANG T Y, et al. Multivariable traffic flow prediction model based on convolutional neural network and gate recurrent unit[J]. Journal of Chongqing University, 2023, 46(8): 132-140. (in Chinese)
    [15]
    SHEN L, SHAO Z, YU Y, et al. Hybrid approach combining modified gravity model and deep learning for short-term forecasting of metro transit passenger flows[J]. Transportation Research Record, 2021, 2675(1): 25-38.
    [16]
    ZHANG J, CHE H, CHEN F, et al. Short-term origin-destination demand prediction in urban rail transit systems: a channel-wise attentive split-convolutional neural network method[J]. Transportation Research Part C: Emerging Technologies, 2021, 124: 102928.
    [17]
    CHU K F, LAM A Y S, LI V O K. Deep multi-scale convolutional LSTM network for travel demand and origin-destination predictions[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(8): 3219-3232.
    [18]
    NOURSALEHI P, KOUTSOPOULOS H N, ZHAO J. Dynamic origin-destination prediction in urban rail systems: a multi-resolution spatio-temporal deep learning approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(6): 5106-5115.
    [19]
    李若怡. 基于改进时空LSTM模型的城市轨道交通系统OD客流短时预测[D]. 北京: 北京交通大学, 2020.

    LI R Y. Short-term prediction of origin-destination passenger flow for urban rail transit based on improved spatio-temporal LSTM model[D]. Beijing: Beijing Jiaotong University, 2020.
    [20]
    DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An Image is worth 16×16 words: transformers for image recognition at scale[EB/OL]. (2021-6-3)[2023-6-19]. https://doi.org/10.48550/arXiv.2010.11929.
    [21]
    VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Advances in Neural Information Processing Systems, 2017, 30: 5998-6008.
    [22]
    HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 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(7)  / Tables(2)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (149) PDF downloads(5) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return