IEICE TRANSACTIONS on Information
Multi Long-Short Term Memory Models for Short Term Traffic Flow Prediction
Summary :
Many single model methods have been applied to real-time short-term traffic flow prediction. However, since traffic flow data is mixed with a variety of ingredients, the performance of single model is limited. Therefore, we proposed Multi-Long-Short Term Memory Models, which improved traffic flow prediction accuracy comparing with state-of-the-art models.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.12 pp.3272-3275
- Publication Date
- 2018/12/01
- Publicized
- 2018/09/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDL8087
- Type of Manuscript
- LETTER
- Category
- Biocybernetics, Neurocomputing
Authors
Zelong XUE
South China University of Technology
Yang XUE
South China University of Technology
Keyword
Latest Issue
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Zelong XUE, Yang XUE, "Multi Long-Short Term Memory Models for Short Term Traffic Flow Prediction" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 12, pp. 3272-3275, December 2018, doi: 10.1587/transinf.2018EDL8087.
Abstract: Many single model methods have been applied to real-time short-term traffic flow prediction. However, since traffic flow data is mixed with a variety of ingredients, the performance of single model is limited. Therefore, we proposed Multi-Long-Short Term Memory Models, which improved traffic flow prediction accuracy comparing with state-of-the-art models.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8087/_p
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@ARTICLE{e101-d_12_3272,
author={Zelong XUE, Yang XUE, },
journal={IEICE TRANSACTIONS on Information},
title={Multi Long-Short Term Memory Models for Short Term Traffic Flow Prediction},
year={2018},
volume={E101-D},
number={12},
pages={3272-3275},
abstract={Many single model methods have been applied to real-time short-term traffic flow prediction. However, since traffic flow data is mixed with a variety of ingredients, the performance of single model is limited. Therefore, we proposed Multi-Long-Short Term Memory Models, which improved traffic flow prediction accuracy comparing with state-of-the-art models.},
keywords={},
doi={10.1587/transinf.2018EDL8087},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Multi Long-Short Term Memory Models for Short Term Traffic Flow Prediction
T2 - IEICE TRANSACTIONS on Information
SP - 3272
EP - 3275
AU - Zelong XUE
AU - Yang XUE
PY - 2018
DO - 10.1587/transinf.2018EDL8087
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2018
AB - Many single model methods have been applied to real-time short-term traffic flow prediction. However, since traffic flow data is mixed with a variety of ingredients, the performance of single model is limited. Therefore, we proposed Multi-Long-Short Term Memory Models, which improved traffic flow prediction accuracy comparing with state-of-the-art models.
ER -
English
