Multi-Step Short-Term Traffic Flow Prediction Based on a Novel Hybrid ARIMA-LSTM Neural Network

Accurate and real-time traffic flow prediction is the foundation for intelligent transportation systems (ITSs). Since traffic flow time series contains both linear and nonlinear patterns, both theoretical and empirical findings have indicated that a combination of different models outperforms individual models. The authors propose a novel hybrid autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) neural network model (ARIMA-LSTM) for multi-step short-term traffic flow prediction. Firstly, they use the ARIMA model to extract linear parts. Then they formulate a novel neural network containing LSTM layers, a concatenation layer, and current linear components and a multi-output layer for multi-step prediction. Finally, the neural network is optimized on a global scale. To test the performance of proposed model, the authors use the freeway traffic volume data and employed individual ARIMA, LSTM models and the hybrid ARIMA-ANN model for comparison. The test results indicate the proposed hybrid ARIMA-LSTM model is a reliable model.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 179-189
  • Monograph Title: CICTP 2020: Advanced Transportation Technologies and Development-Enhancing Connections

Subject/Index Terms

Filing Info

  • Accession Number: 01749754
  • Record Type: Publication
  • ISBN: 9780784482933
  • Files: TRIS, ASCE
  • Created Date: Aug 12 2020 3:01PM