A highly efficient framework for outlier detection in urban traffic flow

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 tra...

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Autores principales:	Xing Wang, Ruihao Zeng, Fumin Zou, Faliang Huang, Biao Jin
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Outlier detection Road traffic flow pattern Nonnegative matrix factorization (NMF) K‐FCM clustering algorithm PSO‐BP neural network Transportation engineering TA1001-1280 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/abd991d7eded4fec8feffdd7a42a5e3a
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spelling	oai:doaj.org-article:abd991d7eded4fec8feffdd7a42a5e3a2021-11-11T10:16:46ZA highly efficient framework for outlier detection in urban traffic flow1751-95781751-956X10.1049/itr2.12109https://doaj.org/article/abd991d7eded4fec8feffdd7a42a5e3a2021-12-01T00:00:00Zhttps://doi.org/10.1049/itr2.12109https://doaj.org/toc/1751-956Xhttps://doaj.org/toc/1751-9578Abstract 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.Xing WangRuihao ZengFumin ZouFaliang HuangBiao JinWileyarticleOutlier detectionRoad traffic flow patternNonnegative matrix factorization (NMF)K‐FCM clustering algorithmPSO‐BP neural networkTransportation engineeringTA1001-1280Electronic computers. Computer scienceQA75.5-76.95ENIET Intelligent Transport Systems, Vol 15, Iss 12, Pp 1494-1507 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Outlier detection Road traffic flow pattern Nonnegative matrix factorization (NMF) K‐FCM clustering algorithm PSO‐BP neural network Transportation engineering TA1001-1280 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Outlier detection Road traffic flow pattern Nonnegative matrix factorization (NMF) K‐FCM clustering algorithm PSO‐BP neural network Transportation engineering TA1001-1280 Electronic computers. Computer science QA75.5-76.95 Xing Wang Ruihao Zeng Fumin Zou Faliang Huang Biao Jin A highly efficient framework for outlier detection in urban traffic flow
description	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.
format	article
author	Xing Wang Ruihao Zeng Fumin Zou Faliang Huang Biao Jin
author_facet	Xing Wang Ruihao Zeng Fumin Zou Faliang Huang Biao Jin
author_sort	Xing Wang
title	A highly efficient framework for outlier detection in urban traffic flow
title_short	A highly efficient framework for outlier detection in urban traffic flow
title_full	A highly efficient framework for outlier detection in urban traffic flow
title_fullStr	A highly efficient framework for outlier detection in urban traffic flow
title_full_unstemmed	A highly efficient framework for outlier detection in urban traffic flow
title_sort	highly efficient framework for outlier detection in urban traffic flow
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/abd991d7eded4fec8feffdd7a42a5e3a
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A highly efficient framework for outlier detection in urban traffic flow

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