Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)

Short-term Traffic Flow Prediction Method Based on Balanced Binary Tree and K-Nearest Neighbor Nonparametric Regression

Authors
Dongfang Fan, Xiaoli Zhang
Corresponding Author
Dongfang Fan
Available Online March 2017.
DOI
https://doi.org/10.2991/msam-17.2017.27How to use a DOI?
Keywords
short-term traffic flow prediction; nonparametric regression; clustering; balanced binary tree
Abstract
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.
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Proceedings
2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
March 2017
ISBN
978-94-6252-324-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/msam-17.2017.27How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

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  - 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  - https://doi.org/10.2991/msam-17.2017.27
ID  - Fan2017/03
ER  -