TY - GEN
T1 - Short term traffic flow prediction based on on-line sequential extreme learning machine
AU - Ma, Zhiyuan
AU - Luo, Guangchun
AU - Huang, Dijiang
N1 - Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2016/4/7
Y1 - 2016/4/7
N2 - Traffic flow cannot be predicted solely based on historical data due to its high dynamics and sensitivity to emergency situations. In this paper, a real traffic data collected from 2011 to 2014 is used, and an adaptive prediction model based on a variant of Extreme Learning Machine (ELM), namely On-line Sequential ELM with forgetting mechanism, is built. The model has the capability of updating itself using incoming data, and adapts to the changes in real time. However, limitations, such as the requirements of large number of neurons and dataset size for initialization, are discovered in practice. To improve the applicability, another scheme involving sequential updating and network reconstruction is proposed. The experimental results show that, compared with the previous method, the proposed one has better performance in time while achieving the similar accuracy.
AB - Traffic flow cannot be predicted solely based on historical data due to its high dynamics and sensitivity to emergency situations. In this paper, a real traffic data collected from 2011 to 2014 is used, and an adaptive prediction model based on a variant of Extreme Learning Machine (ELM), namely On-line Sequential ELM with forgetting mechanism, is built. The model has the capability of updating itself using incoming data, and adapts to the changes in real time. However, limitations, such as the requirements of large number of neurons and dataset size for initialization, are discovered in practice. To improve the applicability, another scheme involving sequential updating and network reconstruction is proposed. The experimental results show that, compared with the previous method, the proposed one has better performance in time while achieving the similar accuracy.
KW - ELM
KW - Traffic flow prediction
KW - adaptive model
KW - online sequential ELM
UR - http://www.scopus.com/inward/record.url?scp=84966671670&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84966671670&partnerID=8YFLogxK
U2 - 10.1109/ICACI.2016.7449818
DO - 10.1109/ICACI.2016.7449818
M3 - Conference contribution
AN - SCOPUS:84966671670
T3 - Proceedings of the 8th International Conference on Advanced Computational Intelligence, ICACI 2016
SP - 143
EP - 149
BT - Proceedings of the 8th International Conference on Advanced Computational Intelligence, ICACI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Advanced Computational Intelligence, ICACI 2016
Y2 - 14 February 2016 through 16 February 2016
ER -