Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow

Autoři: Quanchao Chen aff001;  Di Wen aff001;  Xuqiang Li aff001;  Dingjun Chen aff001;  Hongxia Lv aff001;  Jie Zhang aff001;  Peng Gao aff001
Působiště autorů: School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China aff001;  National Railway Train Diagram Research and Training Center, Southwest Jiaotong University, Chengdu, China aff002;  National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation, Southwest Jiaotong University, Chengdu, China aff003
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222365


Short-term metro passenger flow forecasting is an essential component of intelligent transportation systems (ITS) and can be applied to optimize the passenger flow organization of a station and offer data support for metro passenger flow early warning and system management. LSTM neural networks have recently achieved remarkable recent in the field of natural language processing (NLP) because they are well suited for learning from experience to predict time series. For this purpose, we propose an empirical mode decomposition (EMD)-based long short-term memory (LSTM) neural network model for predicting short-term metro inbound passenger flow. The EMD algorithm decomposes the original sequential passenger flow into several intrinsic mode functions (IMFs) and a residual. Selected IMFs that are strongly correlated with the original data can be obtained via feature selection. The selected IMFs and the original data are integrated into inputs for LSTM neural networks, and a single LSTM prediction model and an EMD-LSTM hybrid forecasting model are developed. Finally, historical real automatic fare collection (AFC) data from metro passengers are collected from Chengdu Metro to verify the validity of the proposed EMD-LSTM prediction model. The results indicate that the proposed EMD-LSTM hybrid forecasting model outperforms the LSTM, ARIMA and BPN models.

Klíčová slova:

Research and analysis methods – Mathematical and statistical techniques – Statistical methods – Physical sciences – Mathematics – Statistics – Computer and information sciences – Recurrent neural networks – Artificial intelligence – Machine learning – Support vector machines – Information technology – Natural language processing – Biology and life sciences – Neuroscience – Neural networks – Cognitive science – Cognition – Memory – Short term memory – Learning and memory – Engineering and technology – Transportation


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2019 Číslo 9

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