Traffic Flow Forecasting Method based on Gradient Boosting Decision Tree
Ying Xia, Jungang Chen
Available Online April 2017.
- https://doi.org/10.2991/fmsmt-17.2017.87How to use a DOI?
- Traffic Flow Forecasting, Sliding Time Window, Temporal Correlation Search, Feature Extension, Gradient Boosting Decision Tree
- Accurate traffic flow forecasting is very important for intelligent transportation system. This paper proposes a traffic flow forecasting method based on gradient boosting decision tree. In the preprocess phase, sliding time window and feature extension of traffic data are designed on the basis of time series analysis and temporal correlation search is introduced to find prediction training set. In the prediction phase, gradient boosting decision tree is used to predict the traffic flow. Experimental results show that the traffic flow forecasting method based on gradient boosting decision tree is effective and can obtain higher prediction accuracy.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Ying Xia AU - Jungang Chen PY - 2017/04 DA - 2017/04 TI - Traffic Flow Forecasting Method based on Gradient Boosting Decision Tree BT - Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017) PB - Atlantis Press SP - 413 EP - 416 SN - 2352-5401 UR - https://doi.org/10.2991/fmsmt-17.2017.87 DO - https://doi.org/10.2991/fmsmt-17.2017.87 ID - Xia2017/04 ER -