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DDFM: A Novel Perspective on Urban Travel Demand Forecasting Based on the Ensemble Empirical Mode Decomposition and Deep Learning

Published: 16 December 2022 Publication History

Abstract

Urban travel demand forecasting is significant in improving traffic conditions and traffic management. Although current research focuses on urban travel demand prediction using various deep learning models, there is high-frequency noise and complex nonlinear fluctuation patterns in urban travel demand time series, and these deep learning models do not have an excellent ability to fit the various patterns and certain local extremes of urban travel demand. Therefore, a Deep Decomposition Forecasting Model (DDFM) is introduced for modeling and predicting non-stationary, non-linear urban travel demand data. The DDFM consists of two main components, which are the Ensemble Empirical Mode Decomposition (EEMD) for decomposing urban travel demand data and the Long Short-term Memory (LSTM) network. EEMD can reduce the LSTM's difficulty in fitting the non-linear fluctuation pattern of the data. LSTM can model long-term time-series data. Experiments show that the performance of DDFM is excellent. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of DDFM in predicting urban travel demand are significantly reduced compared to the baseline. Overall, DDFM can do a good job of predicting urban travel demand with complex nonlinear patterns.

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Cited By

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  • (2023)A Spatio-Temporal Approach for Urban Travel Demand ForecastingComputer Science and Application10.12677/CSA.2023.13305113:03(518-527)Online publication date: 2023
  • (2023)Spatio-Temporal Heterogeneous Graph-Based Convolutional Networks for Traffic Flow ForecastingComputer Science and Application10.12677/CSA.2023.13303813:03(399-409)Online publication date: 2023
  • (2023)Spatial-Temporal Based Deep Learning Model Perceives Travel TimeComputer Science and Application10.12677/CSA.2023.13303613:03(378-389)Online publication date: 2023

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  1. DDFM: A Novel Perspective on Urban Travel Demand Forecasting Based on the Ensemble Empirical Mode Decomposition and Deep Learning

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      cover image ACM Other conferences
      ICBDT '22: Proceedings of the 5th International Conference on Big Data Technologies
      September 2022
      454 pages
      ISBN:9781450396875
      DOI:10.1145/3565291
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      Published: 16 December 2022

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      Author Tags

      1. Complex Nonlinear Fluctuation Patterns
      2. Ensemble Empirical Mode Decomposition
      3. Long Short-term Memory
      4. Urban Travel Demand

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      • (2023)A Spatio-Temporal Approach for Urban Travel Demand ForecastingComputer Science and Application10.12677/CSA.2023.13305113:03(518-527)Online publication date: 2023
      • (2023)Spatio-Temporal Heterogeneous Graph-Based Convolutional Networks for Traffic Flow ForecastingComputer Science and Application10.12677/CSA.2023.13303813:03(399-409)Online publication date: 2023
      • (2023)Spatial-Temporal Based Deep Learning Model Perceives Travel TimeComputer Science and Application10.12677/CSA.2023.13303613:03(378-389)Online publication date: 2023

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