A data mining framework to analyze road accident data

Abstract

Background Road and traffic accidents are uncertain and unpredictable incidents and their analysis requires the knowledge of the factors affecting them. Road and traffic accidents are defined by a set of variables which are mostly of discrete nature. The major problem in the analysis of accident data is its heterogeneous nature [1]. Thus heterogeneity must be considered during analysis of the data otherwise, some relationship between the data may remain hidden. Although, researchers used segmentation of the data to reduce this heterogeneity using some measures such as expert knowledge, but there is no guarantee that this will lead to an optimal segmentation which consists of homogeneous groups of road accidents [2]. Therefore, cluster analysis can assist the segmentation of road accidents. Cluster analysis which is an important data mining technique can be used as a preliminary task to achieve various goals. Karlaftis and Tarko [3] used cluster analysis to categorize the accident data into different categories and further analyzed cluster results using Negative Binomial (NB) to identify the impact of driver age on road accidents.

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.…”

Intelligent Ambulance Fleet Management System

“…The initial placement of ambulances for accident patients is based on accident prone area data [15]. There will be at least one ambulance within 2 km radius of the accident prone area.…”

A Conjoint Analysis of Road Accident Data using K-modes Clustering and Bayesian Networks (Road Accident Analysis using clustering and classification)

“…Transportation systems itself is not responsible for these traffic accidents but several other factors [2,3]. These factors can be defined as environmental factors such as weather and temperature, road specific factors such as road type, road width, and road shoulder width, human factors i.e.…”