A highly efficient framework for outlier detection in urban traffic flow. Issue 12 (17th August 2021)

• Record Type: Journal Article
• Authors: Wang, Xing; Zeng, Ruihao; Zou, Fumin; Huang, Faliang; Jin, Biao
• Is Part Of: IET intelligent transport systems. Volume 15:Issue 12(2021)
• Page Start: 1494
• Page End: 1507
• Publication Date: 2021-08-17
• Subjects: Outlier detection -- Road traffic flow pattern -- Nonnegative matrix factorization (NMF) -- K‐FCM clustering algorithm -- PSO‐BP neural network -- Algebra -- Other topics in statistics -- Combinatorial mathematics -- Optimisation techniques -- Data handling techniques -- Traffic engineering computing -- Neural nets; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/itr2.12109
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract: The outliers in traffic flow represent the anomalies or emergencies in the road. The detection and research of outliers will help to reveal the mechanism of such events. Aiming at the problem of outlier detection in urban traffic flow, this paper innovatively proposes a highly efficient traffic outlier detection framework based on the study of road traffic flow patterns. The main research works are as follows: (1) data pre‐processing, the road traffic flow matrix of the roads is calculated based on the collected GPS data, the non‐negative matrix factorisation algorithm is chosen to reduce the dimension of the matrix. (2) Road traffic flow pattern extraction, the fuzzy C‐means clustering algorithm with the Optimal k‐cluster centre (K‐FCM) is adopted to cluster the roads with the same road traffic flow pattern. (3) Outlier detection model training and evaluation, kernel density estimation is introduced to fit the probability density of roads traffic flow matrices which are used to train the back propagation neural network based on particle swarm optimisation to obtain the outlier detection and evaluation model, and a threshold is introduced to optimise the precision and recall of the model. The experimental results show that: the average precision and recall of the proposed method in this paper are 95.38% and 96.23%, respectively, and the average detection time is 28.4 seconds. The method has high accuracy, high efficiency and good practical significance.