Earticle

초록

영어

Driving speed is a key parameter for building the traffic state identification model, its precision directly affects the model reliability and the traffic state identification accuracy. Aiming at the standard normal deviation method’s defects in dealing with the extreme noise data, an anomaly driving speed detection algorithm based on quantiles is proposed, use historical data to establish the exception borders which are used to detect whether an unknown data is abnormal; on the basis of the abnormal data detection, a driving speed prediction algorithm based on improved KNN is proposed, use K-means algorithm to clustering the historical data, and predict the next moment’s speed according to the distance between the data to be predicted and the clusters, the predicted speed can be used to correct the abnormal speed. Experimental results show that the detection rate of the proposed anomaly detection algorithm has improved about 4.25% compared with the standard normal deviation method, and the false detection rate has reduced about 25.51%; the mean relative error of the proposed speed prediction algorithm is 13.69%, it can predict the driving speed well, namely, the anomaly driving speed detection and correction algorithm based on quantiles and KNN is feasible and effective.

목차

Abstract
 1. Introduction
 2. Anomaly DRIVING speed Detection Algorithm based on Quantiles
 3. Anomaly Driving Speed Correction Algorithm based on Improved KNN
  3.1 Neighbors Selection Method based on K-means Algorithm
  3.2 Measurement of Distance and Determination of Weight Function
  3.3 The Improved Anomaly Driving Speed Correction Algorithm based on KNN
 4. Simulation Experiment and Analysis
  4.1 Anomaly Driving Speed Detection Experiment and Analysis
  4.2 Driving Speed Predicting Experiment and Analysis
 5. Conclusions
 Acknowledgement
 References

키워드

저자

• Guo Yanling [ College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China ]
• Liu Lichen [ 1College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China, Harbin Kejia Universal Electric Mechanical Corporation Co., Ltd., Harbin, China ]
• Gao Meng [ China Mobile Communications Corporation Co., Ltd. of Heilongjiang Branch, Harbin, China ]
• Gao Lewen [ China Mobile Communications Corporation Design Institute Co., Ltd. of Heilongjiang Branch, Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605