The Promotion Effect of LOESS Smoothing Technique in Short-term Traffic Volume Clustering

DOI

10.2991/masta-19.2019.65How to use a DOI?

Keywords

Short-term traffic volume, Clustering, LOESS smoothing technique, Intelligent transport system

Abstract

In short-term traffic volume clustering, one important issue is the representation of traffic profiles. This article focuses on how the LOESS smoothing technique enhances the clustering effect and what the best value of parameter span of LOESS is. This article used K-Means clustering algorithm and compared the clustering effect using raw data and smoothed data. The experiment result shows when the traffic profiles are slight smoothed, the clustering effect enhances from 39.15% to 66.48%. And the best range of parameter span is 0.2~0.4 to keep the balance of clustering effect and profiles details. This article verifies the promotion effect of LOESS smoothing technique in short-term traffic volume clustering and gives advice on the best value of LOESS parameter.

Copyright

© 2019, 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/).

Download article (PDF)

TY  - CONF
AU  - Jia-juan Chen
AU  - Yu-bang Liu
AU  - Zhi-yuan Wang
AU  - Chuan-tao Wang
PY  - 2019/07
DA  - 2019/07
TI  - The Promotion Effect of LOESS Smoothing Technique in Short-term Traffic Volume Clustering
BT  - Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)
PB  - Atlantis Press
SP  - 386
EP  - 389
SN  - 1951-6851
UR  - https://doi.org/10.2991/masta-19.2019.65
DO  - 10.2991/masta-19.2019.65
ID  - Chen2019/07
ER  -