Short-term Traffic Flow Prediction Method Based on Balanced Binary Tree and K-Nearest Neighbor Nonparametric Regression
- 10.2991/msam-17.2017.27How to use a DOI?
- short-term traffic flow prediction; nonparametric regression; clustering; balanced binary tree
Real-time and accurate short-term traffic flow prediction is a key issue and difficult in traffic control and guidance. Using data mining and large data-driven principle, nonparametric regression is a better method to resolve short-term traffic flow prediction. But there are two main obstacles that case base is difficult to be generated and search is slow. For this reason, this paper presents a short-term traffic flow prediction method based on balanced binary tree and K-NEAREST NEIGHBOR NONPARAMETRIC REGRESSION (KNN2NPR). Case base is generated through clustering method and balance binary tree structure. K-nearest neighbor nonparametric regression improves accuracy of prediction and fulfills the real-time requirement. The prediction example in this paper demonstrates that this method is effective.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Dongfang Fan AU - Xiaoli Zhang PY - 2017/03 DA - 2017/03 TI - Short-term Traffic Flow Prediction Method Based on Balanced Binary Tree and K-Nearest Neighbor Nonparametric Regression BT - Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017) PB - Atlantis Press SP - 118 EP - 121 SN - 1951-6851 UR - https://doi.org/10.2991/msam-17.2017.27 DO - 10.2991/msam-17.2017.27 ID - Fan2017/03 ER -