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...
Main Authors: | , , , , |
---|---|
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 |
id |
doaj-71e8d09923854a0f92f60b622a8082c8 |
---|---|
record_format |
Article |
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 |
_version_ |
1714842465404452864 |