Abstract
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.
Highlights
With the gradual improvement in the highway network and the arrival of the information era, the data generated by intelligent toll systems [1], intelligent road detection systems, and other facilities have formed a certain scale [2]. e highway network has become increasingly complex, and the probability of abnormal events has increased [3]
A traffic accident refers to an abnormal traffic condition in which a vehicle crashed [8], while a traffic incident refers to abnormal conditions such as vehicle breakdown, expired parking, equipment failure, and toll evasion [9]. e detection of this type of abnormal event has always been the key to highway electromechanical systems but hard to find
Before the emergence of data mining analysis methods, traffic administrative departments mainly relied on simple sampling and statistical methods to detect events and analyze highway traffic conditions, which resulted in substantial investments and poor application results
Summary
With the gradual improvement in the highway network and the arrival of the information era, the data generated by intelligent toll systems [1], intelligent road detection systems, and other facilities have formed a certain scale [2]. e highway network has become increasingly complex, and the probability of abnormal events has increased [3]. E improved algorithm is used to analyze and verify traffic conditions, detect abnormal events, and identify problems such as vehicle overload, equipment damage, and network failure. It has high recognition accuracy of abnormal events and provides data support for highway operation and management. E density-based fast peak algorithm is based on the assumption that the cluster center feature has a high local density and a large distance from high-density points; this assumption is the basis of the clustering process In this process, the number of classes is intuitively visible, and outliers can be visually presented to facilitate accurate analysis.
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