A Fuzzy-Logic Based Multi-Dimensional Analysis of Traffic Incident Data
Matthew R. Kwiatkowski, Benjamin J. Zacharias, Carson Kai-Sang Leung, PokYee Joey Tsu, Joshua M. Thomas, Michael Kolisnyk, Alfredo Cuzzocrea · 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · 2022
In the current era, big data can be generated and collected anywhere. These include different types of data, which can be mined by data mining. Many interesting important data are multi-dimensional. Many of them capture uncertain or imprecise data, which include traffic incident data. Multi-dimensional data analysis and mining helps reveal factors associating with, or contributing to, traffic accidents. In this paper, we present a fuzzy-logic based multi-dimensional analysis of traffic incident data. Fuzzy logic helps capture uncertainty and vagueness of users. Evaluation on real-life traffic data from a Canadian city demonstrates the practicality and effectiveness of using our fuzzy-logic based multi-dimensional data mining and analysis approach within the domain of traffic incident data.