Accurate short-term passenger flow prediction in urban rail transit (URT) plays an important role in ensuring the stable operation of the URT systems. Because of the complex dynamic spatial-temporal dependencies and potential semantic correlations of the URT network, accurate and effective short-term passenger flow prediction is challenging. To solve these problems, a novel model called the dynamic spatial-temporal graph convolutional network (DSTGCN) was proposed. Firstly, spatial semantic graphs (SSGs) were established to encode the spatial dependencies and semantic correlations of the URT network. Meanwhile, the dynamic graph convolutional network (DGCN) with the spatial attention mechanism was used to learn the dynamic spatial correlations of the nodes in the SSGs. Then, the long short-term memory (LSTM) network was integrated into the DGCN to learn the dynamic changes of passenger flow and capture local temporal dependencies. Moreover, the temporal attention mechanism was introduced after LSTM to capture global dynamic temporal correlations by adjusting the weights of different sequence information. Finally, the full connection layers were used to output the prediction results. Several experiments were conducted on Nanning Metro Line 1 real datasets to evaluate the model. The experimental results showed that the DSTGCN can effectively capture the dynamic spatial-temporal dependencies and semantic associations of the passenger flow. Besides, the prediction performances of the DSTGCN were better than those of existing baseline models, and it can provide technical support for improving the intelligent planning and operation decisions of URT systems.
TanY.LiY.WangR.MiX.LiY.ZhengH.KeY.WangY.Improving Synchronization in High-Speed Railway and Air Intermodality: Integrated Train Timetable Rescheduling and Passenger Flow Forecasting. IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 3, 2022, pp. 2651–2667.
2.
ZhangJ.WangF.WangK.LinW.XuX.ChenC.Data-Driven Intelligent Transportation Systems: A Survey. IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 4, 2011, pp. 1624–1639.
3.
DingC.DuanJ.ZhangY.WuX.YuG.Using an ARIMA-GARCH Modeling Approach to Improve Subway Short-Term Ridership Forecasting Accounting for Dynamic Volatility. IEEE Transactions on Intelligent Transportation Systems, Vol. 19, No. 4, 2018, pp. 1054–1064.
4.
WilliamsB. M.DurvasulaP. K.BrownD. E.Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models. Transportation Research Record: Journal of the Transportation Research Board, 1998. 1644: 132–141.
5.
OuJ.XiaJ.WuY.RaoW.Short-Term Traffic Flow Forecasting for Urban Roads Using Data-Driven Feature Selection Strategy and Bias-Corrected Random Forests. Transportation Research Record: Journal of the Transportation Research Board, 2017. 2645: 157–167.
6.
MengM.ToanT. D.WongY. D.LamS. H.Short-Term Travel-Time Prediction Using Support Vector Machine and Nearest Neighbor Method. Transportation Research Record: Journal of the Transportation Research Board, 2022. 2676: 353–365.
7.
ZhouJ.CuiG.HuS.ZhangZ.YangC.LiuZ.WangL.LiC.SunM.Graph Neural Networks: A Review of Methods and Applications. AI Open, Vol. 1, 2020, pp. 57–81. https://doi.org/10.1016/j.aiopen.2021.01.001.
ZhangZ.LiM.LinX.WangY.HeF.Multistep Speed Prediction on Traffic Networks: A Deep Learning Approach Considering Spatio-Temporal Dependencies. Transportation Research Part C: Emerging Technologies, Vol. 105, 2019, pp. 297–322.
10.
ChenW.ChenL.XieY.CaoW.GaoY.FengX.Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting. Proceedings of the 34th AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 3529–2536.
11.
ZhaoL.SongY.ZhangC.LiuY.WangP.LinT.DengM.LiH.T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. IEEE Transactions on Intelligent Transportation Systems, Vol. 21, No. 9, 2021, pp. 3848–3858.
12.
YuB.YinH.ZhuZ.Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Proc., 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, Margan Kaufmann, San Mateo, CA, 2018, pp. 3634–3640.
13.
WuZ.PanS.LongG.JiangJ.ZhangC.Graph WaveNet for Deep Spatial-Temporal Graph Modeling. Proc., 28th International Joint Conference on Artificial Intelligence, Macao, China, Association for the Advancement of Artificial Intelligence (AAAI), Marina del Rey, CA, 2019, pp. 1907–1913.
14.
GuoS.LinY.FengN.SongC.WanH.Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 922–929.
15.
SongC.LinY.GuoS.WanH.Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting. Proceedings of the 34th AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 914–921.
16.
BengioY.SimardP.FrasconiP.Learning Long-Term Dependencies With Gradient Descent is Difficult. IEEE Transactions on Neural Networks, Vol. 5, No. 2, 1994, pp. 157–166.
17.
LiD.ZhangJ.ZhangQ.WeiX.Classification of ECG Signals Based on 1D Convolution Neural Network. Proc., 19th IEEE International Conference on e-Health Networking, Applications and Services (Healthcom), Dalian, China, IEEE, New York, 2017, pp. 1–6.
18.
VlahogianniE. I.GoliasJ. C.KarlaftisM. G.Short-Term Traffic Forecasting: Overview of Objectives and Methods. Transport Reviews, Vol. 24, No. 5, 2004, pp. 533–555.
19.
BezuglovA.ComertG.Short-Term Freeway Traffic Parameter Prediction: Application of Grey System Theory Models. Expert Systems With Applications, Vol. 62, 2016, pp. 284–292.
20.
SmithB. L.DemetskyM. J.Traffic Flow Forecasting: Comparison of Modeling Approaches. Journal of Transportation Engineering, Vol. 123, No. 4, 1997, pp. 261–266.
21.
vanderVoortM.DoughertyM.WatsonS.Combining Kohonen Maps With ARIMA Time Series Models to Forecast Traffic Flow. Transportation Research Part C: Emerging Technologies, Vol. 5, No. 4, 1996, pp. 307–318.
22.
WilliamsB. M.Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling. Transportation Research Record: Journal of the Transportation Research Board, 2001. 1776: 194–200.
23.
VlahogianniE. I.KarlaftisM. G.Testing and Comparing Neural Network and Statistical Approaches for Predicting Transportation Time Series. Transportation Research Record: Journal of the Transportation Research Board, 2013. 2399: 9–22.
24.
RoosJ.BonnevayS.GavinG.Dynamic Bayesian Networks With Gaussian Mixture Models for Short-Term Passenger Flow Forecasting. Proceedings of the 12th International Conference on Intelligent Systems and Knowledge Engineering, Nanjing, China, IEEE, New York, 2017, pp. 1–8.
25.
HuangW.SongG.HongH.XieK.Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning. IEEE Transactions on Intelligent Transportation Systems, Vol. 15, No. 5, 2014, pp. 2191–2201.
26.
LiuL.ChenR.A Novel Passenger Flow Prediction Model Using Deep Learning Methods. Transportation Research Part C: Emerging Technologies, Vol. 84, 2017, pp. 74–91.
27.
MaX.YuH.WangY.WangY.Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory. PLoS One, Vol. 10, No. 3, 2015, p. e0119044.
28.
MaX.TaoZ.WangY.YuH.WangY.Long Short-Term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data. Transportation Research Part C: Emerging Technologies, Vol. 54, 2015, pp. 187–197.
29.
FuR.ZhangZ.LiL.Using LSTM and GRU Neural Network Methods for Traffic Flow Prediction. Proceedings of the 31st Youth Academic Annual Conference of Chinese Association of Automation, Wuhan, China, IEEE, New York, 2016, pp. 324–328.
30.
HeD.LiuC.JinZ.MaR.ChenY.ShanS.Fault Diagnosis of Flywheel Bearing Based on Parameter Optimization Variational Mode Decomposition Energy Entropy and Deep Learning. Energy, Vol. 239, 2022, p. 122108.
31.
BaiS.KolterJ.KoltunV.An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv Preprint arXiv: 1803.01271, 2018.
32.
ZhaoW.GaoY.JiT.WanX.YeF.BaiG.Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting. IEEE Access, Vol. 7, 2019, pp. 114496–114507.
33.
LuH.GeZ.SongY.JiangD.ZhouT.QinJ.A Temporal-Aware LSTM Enhanced by Loss-Switch Mechanism for Traffic Flow Forecasting. Neurocomputing, Vol. 427, 2021, pp. 169–178.
34.
HeD.ZouZ.ChenY.LiuB.YaoX.ShanS.Obstacle Detection of Rail Transit Based on Deep Learning. Measurement, Vol. 176, 2021, p. 109241.
35.
MaX.DaiZ.HeZ.MaJ.WangY.WangY.Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Sensors, Vol. 17, No. 4, 2017, p. 818.
36.
ChenM.YuX.LiuY.PCNN: Deep Convolutional Networks for Short-Term Traffic Congestion Prediction. IEEE Transactions on Intelligent Transportation Systems, Vol. 19, No. 11, 2018, pp. 3550–3559.
37.
MaX.ZhangJ.DuB.DingC.SunL.Parallel Architecture of Convolutional Bi-Directional LSTM Neural Networks for Network-Wide Metro Ridership Prediction. IEEE Transactions on Intelligent Transportation Systems, Vol. 20, No. 6, 2019, pp. 2278–2288.
38.
YaoH.TangX.WeiH.ZhengG.LiZ.Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 5668–5675.
39.
ZhangJ.ChenF.CuiZ.GuoY.ZhuY.Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit. IEEE Transactions on Intelligent Transportation Systems, Vol. 22, No. 11, 2021, pp. 7004–7014.
40.
ChenP.FuX.WangX.A Graph Convolutional Stacked Bidirectional Unidirectional-LSTM Neural Network for Metro Ridership Prediction. IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 7, 2021, pp. 6950–6962.
LiQ.HanZ.WuX.Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning. Proc., 32nd AAAI Conference on Artificial Intelligence, New Orleans, LA, Association for the Advancement of Artificial Intelligence, Menlo Park, CA, 2018, pp. 3538–3545.
43.
FengX.GuoJ.QinB.LiuT.LiuY.Effective Deep Memory Networks for Distant Supervised Relation Extraction. Proc., 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, Margan Kaufmann, San Mateo, CA, 2017, pp. 4002–4008.
WangH.LiL.PanP.WangY.JinY.Early Warning of Burst Passenger Flow in Public Transportation System. Transportation Research Part C: Emerging Technologies, Vol. 105, 2019, pp. 580–598.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
