The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
실시간 단기 교통 흐름 예측에는 많은 단일 모델 방법이 적용되었습니다. 그러나 교통흐름 데이터는 다양한 요소가 혼합되어 있기 때문에 단일 모델의 성능에는 한계가 있다. 따라서 우리는 최신 모델에 비해 교통 흐름 예측 정확도를 향상시킨 다중 장단기 메모리 모델을 제안했습니다.
Zelong XUE
South China University of Technology
Yang XUE
South China University of Technology
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부
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
부
@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},}
부
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 -