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

PLoS One. 2019 Jun 26;14(6):e0218626. doi: 10.1371/journal.pone.0218626. eCollection 2019.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alberta
  • Cities
  • Data Collection
  • Humans
  • Linear Models
  • Machine Learning
  • Models, Statistical*
  • Motor Vehicles / statistics & numerical data
  • Neural Networks, Computer
  • Regression Analysis
  • Seasons
  • Support Vector Machine
  • Time Factors
  • Transportation / statistics & numerical data*

Grants and funding

This research was supported by the National Natural Science Foundation of China (71701046 to YG), the China Postdoctoral Science Foundation Funded Project (2017M571644; 2018T110427), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_0151), the Fundamental Research Funds for the Central Universities (2242017K40130; YBJJ1533), and the Scientific Innovation Research of College Graduates in Jiangsu Province (KYLX_0173). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.