AdaEnsemble Learning Approach for Metro Passenger Flow Forecasting
- URL: http://arxiv.org/abs/2002.07575v2
- Date: Tue, 10 Mar 2020 13:34:55 GMT
- Title: AdaEnsemble Learning Approach for Metro Passenger Flow Forecasting
- Authors: Shaolong Sun, Dongchuan Yang, Ju-e Guo, Shouyang Wang
- Abstract summary: We present a novel adaptive ensemble (AdaEnsemble) learning approach to accurately forecast the volume of metro passenger flows.
It combines the complementary advantages of variational mode decomposition (VMD), seasonal autoregressive integrated moving averaging (SARIMA), multilayer perceptron network (MLP) and long short-term memory (LSTM) network.
Our proposed AdaEnsemble learning approach not only has the best forecasting performance compared with the state-of-the-art models but also appears to be the most promising and robust based on the historical passenger flow data in Shenzhen subway system.
- Score: 4.415977307120616
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accurate and timely metro passenger flow forecasting is critical for the
successful deployment of intelligent transportation systems. However, it is
quite challenging to propose an efficient and robust forecasting approach due
to the inherent randomness and variations of metro passenger flow. In this
study, we present a novel adaptive ensemble (AdaEnsemble) learning approach to
accurately forecast the volume of metro passenger flows, and it combines the
complementary advantages of variational mode decomposition (VMD), seasonal
autoregressive integrated moving averaging (SARIMA), multilayer perceptron
network (MLP) and long short-term memory (LSTM) network. The AdaEnsemble
learning approach consists of three important stages. The first stage applies
VMD to decompose the metro passenger flows data into periodic component,
deterministic component and volatility component. Then we employ SARIMA model
to forecast the periodic component, LSTM network to learn and forecast
deterministic component and MLP network to forecast volatility component. In
the last stage, the diverse forecasted components are reconstructed by another
MLP network. The empirical results show that our proposed AdaEnsemble learning
approach not only has the best forecasting performance compared with the
state-of-the-art models but also appears to be the most promising and robust
based on the historical passenger flow data in Shenzhen subway system and
several standard evaluation measures.
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