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- W4380624791 abstract "To meet the actual operational needs of public security traffic control departments, we propose an intelligent construction method for traffic control scenarios and introduce its construction process and application value. In this study, traffic control scenarios are split into several constituent elements. The associated elements are found and combined into scenarios based on actual data. To meet the computational efficiency challenges due to the rapid update of massive traffic violation data in the real world, this study applies ordering points to identify the clustering structure algorithm to achieve fast and efficient clustering of latitude and longitude data. Furthermore, the frequent pattern growth algorithm is improved by splitting the database and node exchange based on a Hash table to improve the association rule mining efficiency. The results reveal that both algorithms significantly improve efficiency and accuracy compared with traditional algorithms. By obtaining the refined influence range of traffic violation control scenarios through the clustering method, correlating the environmental factors of scenarios with control behaviors through association rules, and analyzing the intrinsic causes of scenarios based on identified indicators, the efficiency and accuracy of public security traffic control departments in finding and solving problems are effectively improved." @default.
- W4380624791 created "2023-06-15" @default.
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- W4380624791 date "2023-06-14" @default.
- W4380624791 modified "2023-09-28" @default.
- W4380624791 title "Research on intelligent construction method for traffic control scenarios based on traffic violation data mining" @default.
- W4380624791 doi "https://doi.org/10.1117/12.2683924" @default.
- W4380624791 hasPublicationYear "2023" @default.
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