TY  - JOUR
PY  - 2019//
TI  - Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model
JO  - PLoS one
A1  - Song, Zhanguo
A1  - Guo, Yanyong
A1  - Wu, Yao
A1  - Ma, Jing
SP  - e0218626
EP  - e0218626
VL  - 14
IS  - 6
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1932-6203
UR  - http://dx.doi.org/10.1371/journal.pone.0218626
ID  - ref1
ER  -