Highway Event Detection Algorithm Based on Improved Fast Peak Clustering

Aiming at the mining of traffic events based on large amounts of highway data, this paper proposes an improved fast peak clustering algorithm to process highway toll data. The highway toll data are first analyzed, and a data cleaning method based on the sum of similar coefficients is proposed to pro...

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Main Authors: Lili Pei, Zhaoyun Sun, Yuxi Han, Wei Li, Huaixin Zhao
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2021/7318216
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spelling doaj-71e8d09923854a0f92f60b622a8082c82021-03-01T01:14:11ZengHindawi LimitedMathematical Problems in Engineering1563-51472021-01-01202110.1155/2021/7318216Highway Event Detection Algorithm Based on Improved Fast Peak ClusteringLili Pei0Zhaoyun Sun1Yuxi Han2Wei Li3Huaixin Zhao4School of Information EngineeringSchool of Information EngineeringSchool of Information EngineeringSchool of Information EngineeringShaanxi Provincial Department of TransportationAiming at the mining of traffic events based on large amounts of highway data, this paper proposes an improved fast peak clustering algorithm to process highway toll data. The highway toll data are first analyzed, and a data cleaning method based on the sum of similar coefficients is proposed to process the original data. Next, to avoid the shortcomings of the excessive subjectivity of the original algorithm, an improved fast peak clustering algorithm is proposed. Finally, the improved algorithm is applied to highway traffic condition analysis and abnormal event mining to obtain more accurate and intuitive clustering results. Compared with two classical algorithms, namely, the k-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms, as well as the unimproved original fast peak clustering algorithm, the proposed algorithm is faster and more accurate and can reveal the complex relationships among massive data more efficiently. During the process of reforming the toll system, the algorithm can automatically and more efficiently analyze massive toll data and detect abnormal events, thereby providing a theoretical basis and data support for the operation monitoring and maintenance of highways.http://dx.doi.org/10.1155/2021/7318216
collection DOAJ
language English
format Article
sources DOAJ
author Lili Pei
Zhaoyun Sun
Yuxi Han
Wei Li
Huaixin Zhao
spellingShingle Lili Pei
Zhaoyun Sun
Yuxi Han
Wei Li
Huaixin Zhao
Highway Event Detection Algorithm Based on Improved Fast Peak Clustering
Mathematical Problems in Engineering
author_facet Lili Pei
Zhaoyun Sun
Yuxi Han
Wei Li
Huaixin Zhao
author_sort Lili Pei
title Highway Event Detection Algorithm Based on Improved Fast Peak Clustering
title_short Highway Event Detection Algorithm Based on Improved Fast Peak Clustering
title_full Highway Event Detection Algorithm Based on Improved Fast Peak Clustering
title_fullStr Highway Event Detection Algorithm Based on Improved Fast Peak Clustering
title_full_unstemmed Highway Event Detection Algorithm Based on Improved Fast Peak Clustering
title_sort highway event detection algorithm based on improved fast peak clustering
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1563-5147
publishDate 2021-01-01
description Aiming at the mining of traffic events based on large amounts of highway data, this paper proposes an improved fast peak clustering algorithm to process highway toll data. The highway toll data are first analyzed, and a data cleaning method based on the sum of similar coefficients is proposed to process the original data. Next, to avoid the shortcomings of the excessive subjectivity of the original algorithm, an improved fast peak clustering algorithm is proposed. Finally, the improved algorithm is applied to highway traffic condition analysis and abnormal event mining to obtain more accurate and intuitive clustering results. Compared with two classical algorithms, namely, the k-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms, as well as the unimproved original fast peak clustering algorithm, the proposed algorithm is faster and more accurate and can reveal the complex relationships among massive data more efficiently. During the process of reforming the toll system, the algorithm can automatically and more efficiently analyze massive toll data and detect abnormal events, thereby providing a theoretical basis and data support for the operation monitoring and maintenance of highways.
url http://dx.doi.org/10.1155/2021/7318216
work_keys_str_mv AT lilipei highwayeventdetectionalgorithmbasedonimprovedfastpeakclustering
AT zhaoyunsun highwayeventdetectionalgorithmbasedonimprovedfastpeakclustering
AT yuxihan highwayeventdetectionalgorithmbasedonimprovedfastpeakclustering
AT weili highwayeventdetectionalgorithmbasedonimprovedfastpeakclustering
AT huaixinzhao highwayeventdetectionalgorithmbasedonimprovedfastpeakclustering
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