Spatial–temporal traffic outlier detection by coupling road level of service

Chen, Yujun; Pu, Jiexin; Du, Jifang; Zhang, Xiong

doi:10.1049/iet-its.2018.5214

Cited by 7 publications

(4 citation statements)

References 49 publications

(58 reference statements)

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“…The data set

B J 29

is used to conduct comparative experiments in the same experimental environment. The first method is a statistical‐based approach [11] that also studied Beijing traffic data as we do. They introduce a Poisson mixture model (PMM) coupled hidden Markov model (CHMM) outlier detection method to detect traffic anomalies.…”

Section: Methodsmentioning

confidence: 99%

“…The BoostSelect algorithm proposed by Campos et al [10] improved on the Greedy ensemble and SelectV algorithms by using combinatorial statistics for the selection of outliers. There is also an outlier detection algorithm [11] that combined statistical models with Hidden Markov Models that can effectively respond to the influence relationships between road sections.…”

Section: Statistical-based Approachesmentioning

confidence: 99%

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A highly efficient framework for outlier detection in urban traffic flow

Wang

Zeng

Zou

et al. 2021

IET Intelligent Trans Sys

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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.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The data set

B J 29

Section: Methodsmentioning

confidence: 99%

Section: Statistical-based Approachesmentioning

confidence: 99%

A highly efficient framework for outlier detection in urban traffic flow

Wang

Zeng

Zou

et al. 2021

IET Intelligent Trans Sys

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“…There exist many research directions for the trajectory data. Currently, researchers are focusing on optimizing effective trajectory indexing structures [11], and developing methods for trajectory frequent pattern based on grid sequence [12]- [14], trajectory outlier detection based on trajectory information entropy distribution [15], abnormal trajectory detection for intelligent transport system [16], trajectory uncertainty management [17], [18], and mining knowledge from trajectory data [19], [20], etc. Among these domains, the study of abnormal trajectories is an important research direction.…”

Section: Related Workmentioning

confidence: 99%

Abnormal Trajectory Detection Based on a Sparse Subgraph

et al. 2020

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Traditional abnormal trajectory detection algorithms mainly involve the measurement of a single feature; however, the influence of other features on abnormal trajectory is ignored, resulting in the inability to fully discover the abnormal trajectory in the trajectory database. To overcome this limitation, we propose an abnormal trajectory detection method-called TADSS-to find the hidden abnormal trajectory by using a comprehensive measurement. Firstly, we employ three kernel functions to measure the time, velocity and position feature values of trajectory data, where the kernel functions extract semantic feature of the position, time feature of trajectory, and velocity feature of object motion from each trajectory data. Secondly, we propose a feature fusion strategy to measure the similarity of trajectory data, where we assign weights to the above kernel functions and then use a linear combination approach to fuse the weighted kernel functions. Thirdly, we build a trajectory feature graph by using the above fused kernel functions, and then divide the trajectory feature graph into a plurality of subgraphs by using a conventional graph clustering technique. Last, we propose a sparse subgraph method to detect abnormal trajectory, where a novel weight coefficient concept is used to distinguish sparse subgraph. Experimental results driven by both the vehicle trajectory data of Shanghai city and the Atlantic hurricane data demonstrate the performance of our TADSS.

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“…For the detection of abnormal data in the field of transportation, scholars both at home and abroad have conducted a lot of research. Among them, taking the spatial-temporal characteristics of data as the starting point [8], Yu et al proposed a twostage anomaly detection model, which can identify individual trajectory anomalies and group trajectory anomalies in the trajectory data set [9]. Cluster analysis is a commonly used method to classify anomalous data.…”

Section: Introductionmentioning

confidence: 99%

Abnormal recognition of the ETC gantry license plate data based on an improved DBSCAN

Yan,

Rui,

et al. 2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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Electronic non-stop toll collection system(ETC)is the most popular highway toll collection system at home and abroad. Based on ETC gantry license plate data, this paper systematically describes the highway toll collection process and points out that abnormal data in the license plate data will significantly reduce the accuracy of subsequent data fusion and toll collection. And summarizes the abnormal situation of ETC gantry license plate data. Aiming at the spatial-temporal characteristics of such data, the ETC gantry license plate data abnormality recognition method based on an improved DBSCAN is proposed to realize the adaptive clustering separation of data, which could extract the average travel speed feature set and traffic volume feature set to recognize the abnormal data. The results show that the recognition rates of average speed and traffic volume reaches 96.47% and 88.47%, and the false recognize rate decreases to 0.71% and 0.91%, which could consider data spatial-temporal correlation for abnormal data recognition and solve the problems existing on the current application of ETC gantry data.

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Spatial–temporal traffic outlier detection by coupling road level of service

Cited by 7 publications

References 49 publications

A highly efficient framework for outlier detection in urban traffic flow

A highly efficient framework for outlier detection in urban traffic flow

Abnormal Trajectory Detection Based on a Sparse Subgraph

Abnormal recognition of the ETC gantry license plate data based on an improved DBSCAN

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