has been cited by the following article(s):
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[1]
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Short-term urban rail transit passenger flow forecasting based on fusion model methods using univariate time series
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Applied Soft Computing,
2023 |
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[2]
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Mul-DesLSTM: An integrative multi-time granularity deep learning prediction method for urban rail transit short-term passenger flow
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Engineering Applications of Artificial …,
2023 |
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[3]
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Estimating urban rail transit passenger inflow caused by special events occurrences fusing multi-source data
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Neural Computing and Applications,
2023 |
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[4]
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基于多图时空注意力的轨道交通客流预测模型
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郑州大学学报 (理学版),
2023 |
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[5]
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Short-term passenger flow prediction of urban rail transit based on SDS-SSA-LSTM
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Journal of Advanced …,
2022 |
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[6]
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Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM.
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Journal of Advanced …,
2022 |
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[7]
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On this page
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Journal of Environmental and Public Health,
2022 |
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[8]
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A novel prediction model for the inbound passenger flow of urban rail transit
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2021 |
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[9]
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A Systematic Literature Review of Metro's Passenger Flow Prediction
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2020 |
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[10]
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Urban Rail Transit Demand Analysis and Prediction: A Review of Recent Studies
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Intelligent Interactive Multimedia Systems and Services,
2018 |
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[11]
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Dynamic Bayesian networks with Gaussian mixture models for short-term passenger flow forecasting
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2017 |
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[12]
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A dynamic Bayesian network approach to forecast short-term urban rail passenger flows with incomplete data
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Transportation Research Procedia,
2017 |
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[13]
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Prediction for Passenger Flow at the Airport Based on Different Models
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Parallel Architecture, Algorithm and Programming,
2017 |
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[14]
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Enriching Play Experience across Multiple Platforms through Collaborative Real World Actions
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2016 |
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[15]
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Short-Term Urban Rail Passenger Flow Forecasting: A Dynamic Bayesian Network Approach
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2016 |
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[1]
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Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM
Journal of Advanced Transportation,
2022
DOI:10.1155/2022/2589681
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[2]
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A dynamic Bayesian network approach to forecast short-term urban rail passenger flows with incomplete data
Transportation Research Procedia,
2017
DOI:10.1016/j.trpro.2017.07.008
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[3]
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Dynamic Bayesian networks with Gaussian mixture models for short-term passenger flow forecasting
2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE),
2017
DOI:10.1109/ISKE.2017.8258756
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[4]
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Short-Term Urban Rail Passenger Flow Forecasting: A Dynamic Bayesian Network Approach
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA),
2016
DOI:10.1109/ICMLA.2016.0187
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