Highway Event Detection Algorithm Based on Improved Fast Peak Clustering

Mathematical Problems in Engineering: Theory, Methods and Applications

article

Lili Pei¹  Zhaoyun Sun¹  Yuxi Han¹  Wei Li¹  Huaixin Zhao² 

[1] School of Information Engineering, Chang’an University;Shaanxi Provincial Department of Transportation

DOI  :  10.1155/2021/7318216

来源: Hindawi Publishing Corporation

【 摘 要 】

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.

【 授权许可】

Unknown