Short-Term Traffic Flow Forecasting Considering Upstream Traffic Information
Fei Kou, Weixiang Xu, Huiting Yang
Available Online March 2018.
- https://doi.org/10.2991/mecae-18.2018.86How to use a DOI?
- short-term traffic flow forecasting, K-NN model, upstream information.
- Timely and accurately short-term traffic flow forecasting is very important in the application of the Intelligent Transportation System (ITS). In this paper, an improved k-nearest neighbor (K-NN) model considering upstream traffic information was proposed to forecast short-term traffic flow. In this study, the traffic state of a road segment was described as a state matrix with upstream information instead of only a time series vector. And the weighted Euclidean distance gave different weight to target and upstream road segment was used to measure the similarity between the target state matrix and historical state matrix. The K-NN model was trained by the training data to determine the optimal K value. This study used the reverse distance weighted average method based on similarity of the neighbors to generate the forecasting traffic flow in the future time steps. The same traffic data was used to compare the improved model with three models.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Fei Kou AU - Weixiang Xu AU - Huiting Yang PY - 2018/03 DA - 2018/03 TI - Short-Term Traffic Flow Forecasting Considering Upstream Traffic Information PB - Atlantis Press SP - 560 EP - 564 SN - 2352-5401 UR - https://doi.org/10.2991/mecae-18.2018.86 DO - https://doi.org/10.2991/mecae-18.2018.86 ID - Kou2018/03 ER -