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 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.

Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities

“…The framework used the non negative matrix decomposition algorithm to pre-process the data, used the fuzzy C-means clustering algorithm with the optimal K-cluster center to extract the road TF patterns, and used kernel density estimation to fit the probability density of the road TF matrix. The laboratory findings expressed that the mean accuracy and recall of this method were 95% and 96.%, respectively [14]. To better detect unauthorized wireless cameras, experts such as Y. Cheng proposed a lightweight and effective detection mechanism based on smartphones and a human assisted recognition model, which utilized the inherent traffic patterns of wireless camera traffic.…”