Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)

Traffic Flow Forecasting Method based on Gradient Boosting Decision Tree

Authors
Ying Xia, Jungang Chen
Corresponding Author
Ying Xia
Available Online April 2017.
DOI
https://doi.org/10.2991/fmsmt-17.2017.87How to use a DOI?
Keywords
Traffic Flow Forecasting, Sliding Time Window, Temporal Correlation Search, Feature Extension, Gradient Boosting Decision Tree
Abstract
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.

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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  -