A novel framework to analyze road accident time series data

A new approach to the space–time analysis of big data: application to subway traffic data in Seoul

“…Finally, one issue in dealing with big data is a reduction of dimensions [23,24]. The CSEOF analysis has reduced the facets of spatio-temporal variability; majority (~ 99%) of subway passenger variability is nicely summarized in terms of the first four CSEOF modes.…”

Improved sqrt-cosine similarity measurement

“…Chebyshev distance is defined on a vector space where the distance between two vectors is the greatest of difference along any coordinate dimension [12]. Triangle distance is considered as the cosine of a triangle between two vectors and its value range between 0 and 2 [13]. The Bray-Curtis similarity measure [14] which is sensitive to outlying values is a city-block metric.…”

Mining and prioritization of association rules for big data: multi-criteria decision analysis approach

“…Empirical study: road accident analysis (Table 3) in order to identify the main factors that affect road accident [26][27][28][29][30]. The variables describe characteristics related to the accident (type and cause), the driver (age, sex, and experience), vehicle (age and type), road (condition and geometry), time, season, number of injuries/death, etc.…”