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Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model

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  • Zhanguo Song
  • Yanyong Guo
  • Yao Wu
  • Jing Ma

Abstract

Short-term traffic speed prediction is a key component of proactive traffic control in the intelligent transportation systems. The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from an urban freeway in Edmonton, Canada. A seasonal autoregressive integrated moving average plus seasonal discrete grey model structure (SARIMA-SDGM) was proposed to perform the traffic speed prediction. The model performance of SARIMA-SDGM model was compared with that of the seasonal autoregressive integrated moving average (SARIMA) model, seasonal discrete grey model (SDGM), artificial neural network (ANN) model, and support vector regression (SVR) model. The results showed that SARIMA-SDGM model performs best with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE). The traffic speed prediction accuracy under different time intervals were compared based on the SARIMA-SDGM model. The results showed that the prediction accuracy improves with the increase in time interval. In addition, when the time interval is greater than 10 min, the prediction results yield stable prediction accuracy.

Suggested Citation

  • Zhanguo Song & Yanyong Guo & Yao Wu & Jing Ma, 2019. "Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-19, June.
    • Handle: RePEc:plo:pone00:0218626
    DOI: 10.1371/journal.pone.0218626
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    References listed on IDEAS

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    1. Dougherty, Mark S. & Cobbett, Mark R., 1997. "Short-term inter-urban traffic forecasts using neural networks," International Journal of Forecasting, Elsevier, vol. 13(1), pages 21-31, March.
    2. Yanyong Guo & Yao Wu & Jian Lu & Jibiao Zhou, 2019. "Modeling the Unobserved Heterogeneity in E-bike Collision Severity Using Full Bayesian Random Parameters Multinomial Logit Regression," Sustainability, MDPI, vol. 11(7), pages 1-12, April.
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    1. Quang Hoc Tran & Yao-Min Fang & Tien-Yin Chou & Thanh-Van Hoang & Chun-Tse Wang & Van Truong Vu & Thi Lan Huong Ho & Quang Le & Mei-Hsin Chen, 2022. "Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    2. Taghreed Alghamdi & Sifatul Mostafi & Ghadeer Abdelkader & Khalid Elgazzar, 2022. "A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior," Future Internet, MDPI, vol. 14(10), pages 1-21, October.
    3. Taghreed Alghamdi & Khalid Elgazzar & Taysseer Sharaf, 2021. "Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling," Future Internet, MDPI, vol. 13(9), pages 1-18, August.

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