基于VMD-ISSA-BiLSTM的高速铁路短时客流预测

林立; 孟学雷; 韩正; 夏溪蔓; 李璐; 高如虎

doi:10.19713/j.cnki.43-1423/u.T20260200

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基于VMD-ISSA-BiLSTM的高速铁路短时客流预测

高速铁路技术 | 更新时间：2026-07-24

- 基于VMD-ISSA-BiLSTM的高速铁路短时客流预测
- Short-term passenger flow prediction for high-speed railway based on VMD-ISSA-BiLSTM
- 铁道科学与工程学报 2026年23卷第7期页码：3087-3098
- 作者机构：
  
  1.山东交通学院轨道交通学院，山东济南 250357
  2.兰州交通大学机电工程学院机械工程博士后流动站，甘肃兰州 730070
  3.兰州交通大学交通运输学院，甘肃兰州 730070
- 作者简介：
  
  孟学雷(1979—)，男，山东泰安人，教授，博士，从事轨道交通运行管理和决策优化研究；E-mail：mxl@mail.lzjtu.cn
- 基金信息：
  
  甘肃省科技计划资助项目(25JRRA220);国家自然科学基金资助项目(72361020);中央引导地方科技发展资金项目(25ZYJA015)
- DOI：10.19713/j.cnki.43-1423/u.T20260200
  中图分类号： U293.13
- 收稿：2026-02-10，
  
  网络首发：2026-07-24，
  
  纸质出版：2026-07-28
- 稿件说明：
移动端阅览
林立,孟学雷,韩正等.基于VMD-ISSA-BiLSTM的高速铁路短时客流预测[J].铁道科学与工程学报,2026,23(07):3087-3098.

LIN Li,MENG Xuelei,HAN Zheng,et al.Short-term passenger flow prediction for high-speed railway based on VMD-ISSA-BiLSTM[J].Journal of Railway Science and Engineering,2026,23(07):3087-3098.
林立,孟学雷,韩正等.基于VMD-ISSA-BiLSTM的高速铁路短时客流预测[J].铁道科学与工程学报,2026,23(07):3087-3098. DOI： 10.19713/j.cnki.43-1423/u.T20260200.

LIN Li,MENG Xuelei,HAN Zheng,et al.Short-term passenger flow prediction for high-speed railway based on VMD-ISSA-BiLSTM[J].Journal of Railway Science and Engineering,2026,23(07):3087-3098. DOI： 10.19713/j.cnki.43-1423/u.T20260200.

摘要

针对高速铁路客流动态波动、非平稳特征以及传统预测模型在复杂环境下鲁棒性不足的问题，本文构建了一种基于多尺度数据特征的客流预测模型。该模型首先将原始客流数据经变分模态分解处理，生成具有差异化频率特性及复杂度的本征模态函数。其次，设计改进的麻雀搜索算法，优化双向长短期记忆网络的关键超参数，增强模型对时序关系的捕捉能力。然后利用全连接层融合多维预测结果，构建组合预测模型。最后以广珠城际铁路客票数据为样本，在15、30和60 min三种时间粒度下，将所提模型与7种经典预测模型进行对比分析。计算结果表明，本文模型在不同时间尺度下，均保持良

好且稳定的预测效果，尤其是短时间粒度下优势更为突出。在15 min时间粒度下，较之卷积长短期记忆网络(convolutional long short-term memory

ConvLSTM)、门控循环单元(gated recurrent unit

GRU)和自回归求和移动平均模型(autoregressive integrated moving average

ARIMA)，决定系数分别上升了5.18%、36.49%和54.74%。消融实验中，移除变分模态分解算法分支后，模型的平均绝对误差

mae

、均方根误差

rmse

和平均绝对百分比误差

mape

指标依次上升了39.035、36.474和2.699；移除参数动态优化分支后，模型的

mae

、

rmse

和

mape

评价指标分别上升了87.992、99.708和11.315。本文模型能有效捕捉客流的多尺度特征，为高速铁路运输组织和管理决策提供可靠数据支持，助力运输服务质量与乘客出行满意度提升。

Abstract

To aim at the problems of dynamic fluctuations and non-stationary characteristics of high-speed railway passenger flow

as well as the insufficient robustness of traditional prediction models in complex environments

a short-term passenger flow prediction model that integrates multi-scale data features was proposed. The model first adopted the variational mode decomposition algorithm to decompose the original passenger flow time series data into multiple modal functions with different frequencies and complexities. Next

an improved sparrow search algorithm was employed to enhance important hyperparameters in the bidirectional long short-term memory network

which enhancing the model’s ability to capture temporal dependencies. A fully connected layer was then used to integrate multi-dimensional forecast results. The combined prediction model was constructed. Finally

passenger ticket data from the Guangzhou-Zhuhai Intercity Railway was used as a case study. The model was compared with seven classic prediction models at three different time granularities of 15 minutes

30 minutes

and 60 minutes. The calculation results show that the proposed model can exhibit good and stable forecasting performance at different time granularities

with its advantages being more pronounced at shorter time granularities. At the 15-minute time granularity

compared to the C

onvolutional Long Short-Term Memory (ConvLSTM)

Gated Recurrent Unit (GRU)

and Autoregressive Integrated Moving Average (ARIMA) models

the coefficient of determination can increase by 5.18%

36.49%

and 54.74%

respectively

for each comparison. In the ablation experiments

after removing the variational mode decomposition algorithm branch

the model’s mean absolute error (

mae

)

root mean square error (

rmse

)

and mean absolute percentage error (

mape

) metrics can increase by 39.035

36.474

and 2.699

respectively. After removing the parameter dynamic optimization branch

the model’s

mae

rmse

and

mape

evaluation metrics can increase by 87.992

99.708

and 11.315

respectively. The proposed model in this paper can effectively capture the multi-scale features of passenger flow. The method can provide reliable data support for high-speed rail transportation organization and management decision-making

contributing to the enhancement of transport service quality and passenger satisfaction.

关键词

Keywords

references

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 Chunyan , XIE Yawei , SHAN Jun , 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 .

CHEN Enhui , YE Zhirui , WANG Chao , et al . Subway passenger flow prediction for special events using smart card data [J ] . IEEE Transactions on Intelligent Transportation Systems , 2020 , 21 ( 3 ): 1109 - 1120 .

SHAHRIARI S , GHASRI M , SISSON S A , et al . Ensemble of ARIMA: combining parametric and bootstrapping technique for traffic flow prediction [J ] . Transportmetrica A: Transport Science , 2020 , 16 ( 3 ): 1552 - 1573 .

YANG Linchuan , YU Bingjie , LIANG Yuan , et al . Time-varying and non-linear associations between metro ridership and the built environment [J ] . Tunnelling and Underground Space Technology , 2023 , 132 : 104931 .

许心越 , 孔庆雪 , 李建民 , 等 . 建成环境对轨道交通客流的时空异质性影响分析 [J ] . 交通运输系统工程与信息 , 2023 , 23 ( 4 ): 194 - 202 .

XU Xinyue , KONG Qingxue , LI Jianmin , et al . Analysis of spatio-temporal heterogeneity impact of built environment on rail transit passenger flow [J ] . Journal of Transportation Systems Engineering and Information Technology , 2023 , 23 ( 4 ): 194 - 202 .

惠阳 , 王永岗 , 彭辉 , 等 . 基于优化PSO-BP算法的耦合时空特征下地铁客流预测 [J ] . 交通运输工程学报 , 2021 , 21 ( 4 ): 210 - 222 .

HUI Yang , WANG Yonggang , PENG Hui , et al . Subway passenger flow prediction based on optimized PSO-BP algorithm with coupled spatial-temporal characteristics [J ] . Journal of Traffic and Transportation Engineering , 2021 , 21 ( 4 ): 210 - 222 .

王金水 , 欧雪雯 , 陈俊岩 , 等 . 基于时空超图卷积模型的城市轨道站点客流预测 [J ] . 铁道科学与工程学报 , 2023 , 20 ( 12 ): 4506 - 4516 .

WANG Jinshui , OU Xuewen , CHEN Junyan , et al . Passenger flow forecast of urban rail transit stations based on spatio-temporal hypergraph convolution model [J ] . Journal of Railway Science and Engineering , 2023 , 20 ( 12 ): 4506 - 4516 .

胡明伟 , 何国庆 , 吴雯琳 , 等 . 基于改进Logistic-SSA-BP神经网络的地铁短时客流预测研究 [J ] . 重庆交通大学学报(自然科学版) , 2023 , 42 ( 3 ): 90 - 97 .

HU Mingwei , HE Guoqing , WU Wenlin , et al . Subway short-term passenger flow forecast based on improved Logistic-SSA-BP neural network [J ] . Journal of Chongqing Jiaotong University (Natural Sciences) , 2023 , 42 ( 3 ): 90 - 97 .

杨越迪 , 潘保霏 , 刘军 , 等 . 多分辨率多粒度时空特征提取的轨道交通短时OD客流预测方法 [J ] . 铁道学报 , 2024 , 46 ( 11 ): 1 - 11 .

YANG Yuedi , PAN Baofei , LIU Jun , et al . A short-term OD passenger flow prediction method for rail transit based on multi-resolution and multi-granularity spatio-temporal feature extraction [J ] . Journal of the China Railway Society , 2024 , 46 ( 11 ): 1 - 11 .

程国柱 , 吕岩峰 , 冯天军 . 灾害性天气条件下城市轨道交通客流DRN-BiLSTM预测模型 [J ] . 哈尔滨工业大学学报 , 2025 , 57 ( 7 ): 153 - 161 .

CHENG Guozhu , LÜ Yanfeng , FENG Tianjun . Urban rail transit passenger flow DRN-BiLSTM combined forecasting model under catastrophic weather conditions [J ] . Journal of Harbin Institute of Technology , 2025 , 57 ( 7 ): 153 - 161 .

KWAK S , LI Danya , GEROLIMINIS N . A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting [J ] . Scientific Reports , 2024 , 14 : 31624 .

CAO Congqi , SUN Ze , HU Lanshu , et al . A novel deep learning-based framework for five-day regional weather forecasting [J ] . Atmospheric and Oceanic Science Letters , 2026 , 19 ( 1 ): 100653 .

LI Jian , BENNA M K , MATTAR M G . Discovering cognitive strategies with tiny recurrent neural networks [J ] . Nature , 2025 , 644 ( 8078 ): 993 - 1001 .

RADHAKRISHNAN A , BEAGLEHOLE D , PANDIT P , et al . Mechanism for feature learning in neural networks and backpropagation-free machine learning models [J ] . Science , 2024 , 383 ( 6690 ): 1461 - 1467 .

YANG Haofan , DILLON T S , CHEN Y P . Optimized structure of the traffic flow forecasting model with a deep learning approach [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2017 , 28 ( 10 ): 2371 - 2381 .

NOURSALEHI P , KOUTSOPOULOS H N , ZHAO Jinhua . Dynamic origin-destination prediction in urban rail systems: a multi-resolution spatio-temporal deep learning approach [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 23 ( 6 ): 5106 - 5115 .

MA Changxi , DAI Guowen , ZHOU Jibiao . Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 23 ( 6 ): 5615 - 5624 .

ZHANG Enwei , LV Zhiqiang , CHENG Zesheng , et al . CL-DGCN: contrastive learning based deeper graph convolutional network for traffic flow data prediction [J ] . Transportation Research Part E: Logistics and Transportation Review , 2025 , 203 : 104345 .

ALI A , YASIR NAEEM H M , SHARAFIAN A , et al . Dynamic multi-graph spatio-temporal learning for citywide traffic flow prediction in transportation systems [J ] . Chaos , Solitons & Fractals, 2025 , 199 : 116898 .

王江锋 , 丁卫东 , 罗冬宇 , 等 . 基于超图卷积的跨交通方式客流联合预测模型 [J ] . 华南理工大学学报(自然科学版) , 2024 , 52 ( 11 ): 83 - 94 .

WANG Jiangfeng , DING Weidong , LUO Dongyu , et al . Joint prediction model of multi-modal transportation passenger flow based on hypergraph convolution [J ] . Journal of South China University of Technology (Natural Science Edition) , 2024 , 52 ( 11 ): 83 - 94 .

禹倩 , 张亚东 , 郭进 , 等 . 基于深度集成神经网络的城市轨道交通短时进站客流预测 [J ] . 铁道学报 , 2023 , 45 ( 12 ): 37 - 46 .

YU Qian , ZHANG Yadong , GUO Jin , et al . Short-term inbound passenger flow forecasting for urban rail transit based on deep ensemble neural network [J ] . Journal of the China Railway Society , 2023 , 45 ( 12 ): 37 - 46 .

林立 , 孟学雷 , 高如虎 , 等 . 基于多尺度动态时空神经网络的OD客流预测 [J ] . 铁道科学与工程学报 , 2024 , 21 ( 12 ): 4924 - 4935 .

LIN Li , MENG Xuelei , GAO Ruhu , et al . OD passenger flow prediction based on multi-scale dynamic spatio-temporal neural network [J ] . Journal of Railway Science and Engineering , 2024 , 21 ( 12 ): 4924 - 4935 .

石俊刚 , 朱梦琦 . 基于因果驱动的时空图网络地铁出站短时客流预测 [J/OL ] . 铁道科学与工程学报 , 2026-04-03 . https://doi.org/10.19713/j.cnki.43-1423/u.T20251980 https://doi.org/10.19713/j.cnki.43-1423/u.T20251980 .

SHI Jungang , ZHU Mengqi . A causality-driven spatiotemporal graph network for short-term metro exit passenger flow forecasting [J/OL ] . Journal of Railway Science and Engineering , 2026-04-03 . https://doi.org/10.19713/j.cnki.43-1423/u.T20251980 https://doi.org/10.19713/j.cnki.43-1423/u.T20251980 .

车秉泽 , 钱名军 , 王全能 . 多维兴趣点驱动的地铁客流多通道集成预测方法 [J ] . 铁道科学与工程学报 , 2026 , 23 ( 3 ): 1111 - 1124 .

CHE Bingze , QIAN Mingjun , WANG Quanneng . A multi-channel integrated prediction method for metro passenger flow driven by multi-dimensional point of interest [J ] . Journal of Railway Science and Engineering , 2026 , 23 ( 3 ): 1111 - 1124 .

张金雷 , 杨咏杰 , 阴佳腾 , 等 . 基于多时序模式客流时空交互和生成对抗网络的城轨短时进站流预测模型 [J ] . 系统工程理论与实践 , 2026 , 46 ( 3 ): 1271 - 1290 .

ZHANG Jinlei , YANG Yongjie , YIN Jiateng , et al . Multi-patterns spatiotemporal interaction and generative adversarial network based short-term inflow prediction of urban rail transit model [J ] . Systems Engineering-Theory & Practice , 2026 , 46 ( 3 ): 1271 - 1290 .

李之红 , 郄堃 , 王健宇 , 等 . 建成环境影响下的城市轨道交通客流多步短时预测 [J ] . 交通运输系统工程与信息 , 2025 , 25 ( 1 ): 160 - 172 .

LI Zhihong , QIE Kun , WANG Jianyu , et al . Multistep short-term prediction of urban rail transit passenger flow under influence of built environment [J ] . Journal of Transportation Systems Engineering and Information Technology , 2025 , 25 ( 1 ): 160 - 172 .

张颖超 , 安然 , 陈昕 , 等 . 基于多时距风速波动过程划分的高铁沿线风速预测 [J ] . 交通运输工程学报 , 2025 , 25 ( 3 ): 362 - 379 .

ZHANG Yingchao , AN Ran , CHEN Xin , et al . Wind speed prediction along high-speed railway based on multi-time-interval wind speed fluctuation process division [J ] . Journal of Traffic and Transportation Engineering , 2025 , 25 ( 3 ): 362 - 379 .

苏焕银 , 彭舒婷 , 曾琼芳 , 等 . 基于VMD-LSTM混合模型的城际高速铁路时变客流预测 [J ] . 铁道科学与工程学报 , 2023 , 20 ( 4 ): 1200 - 1210 .

SU Huanyin , PENG Shuting , ZENG Qiongfang , et al . Forecast of time-dependent passenger flow of Intercity high-speed railway based on VMD-LSTM mixed model [J ] . Journal of Railway Science and Engineering , 2023 , 20 ( 4 ): 1200 - 1210 .

郭庆辉 , 李媛 , 杨东升 . 一种改进麻雀搜索算法的收敛性分析及应用 [J ] . 控制与决策 , 2024 , 39 ( 8 ): 2502 - 2510 .

GUO Qinghui , LI Yuan , YANG Dongsheng . Convergence analysis and application of an improved sparrow search algorithm [J ] . Control and Decision , 2024 , 39 ( 8 ): 2502 - 2510 .

王庆荣 , 王俊杰 , 朱昌锋 , 等 . 基于BWO-VMD-ISSA-LSTM的交通运输业碳排放预测研究 [J ] . 计算机工程与应用 , 2026 , 62 ( 10 ): 376 - 388 .

WANG Qingrong , WANG Junjie , ZHU Changfeng , et al . Research on transportation carbon emission prediction model based on BWO-VMD-ISSA-LSTMM [J ] . Computer Engineering and Applications , 2026 , 62 ( 10 ): 376 - 388 .

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