Citation

Gong XY, Tang SM. Zhongguo Gonglu Xuebao 2003; 16(1): 82-86.

Affiliation

Intell. Control. and Syst. Eng. Cent, Inst. of Automat. of Acad. of Sci., Beijing 100080, China

Copyright

(Copyright © 2003, Chang'an da xue)

DOI

unavailable

PMID

unavailable

Abstract

Although short-term traffic flow forecasting algorithm is widely used today, some questions still exist. To address these issues, an KNN-NPRCK Nearest Neighbors-Non-Parametric Regression) based integrated algorithm which can make traffic flow forecasting and traffic event detection at the same time is proposed. Then in order to improve the accuracy and computing speed of the proposed algorithm, authors put forward two improvements which are improved in variable K search method based on 'dense degree' and advanced data structures based on dynamic cluster method and hash-function transformation. Field test fully proves two improvements, which the proposed algorithm can adequately meet real-time system requirements and accuracy requirements.