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

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 a...

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Bibliographic Details
Published in:PloS one 2019-06, Vol.14 (6), p.e0218626-e0218626
Main Authors: Song, Zhanguo, Guo, Yanyong, Wu, Yao, Ma, Jing
Format: Article
Language:English
Subjects:
Alberta
Analysis
Artificial neural networks
Biology and Life Sciences
Cities
Computer and Information Sciences
Data Collection
Engineering and Technology
Highways
Humans
Intelligent transportation systems
Intervals
Linear Models
Machine Learning
Models, Statistical
Motor Vehicles - statistics & numerical data
Neural networks
Neural Networks, Computer
Performance prediction
Physical Sciences
Prediction models
Regression Analysis
Regression models
Research and Analysis Methods
Root-mean-square errors
Seasons
Support Vector Machine
Support vector machines
Time Factors
Traffic congestion
Traffic control
Traffic engineering
Traffic flow
Traffic models
Traffic speed
Transportation - statistics & numerical data
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0218626