Abstract
Extracting driver collision patterns by gender and age regarding offences, collision type and injury severity is very useful in road safety, providing a better understanding on behavior of the different driver groups. Self-Organizing Map (SOM) is the tool proposed for distributing and projecting 145,904 drivers according to 8 offence variables on a 2D map. Thus, drivers who are close in the original 8D space (one dimension per offence variable), will remain so in the projected one (2D). Multivariate driving and collision patterns are explored to support the development of future measures to improve road safety. Tests of proportions are used for shedding light on clusters where driver offence is present. Finally, the SOM results were compared for validation with those of the standard K-Means clustering technique. The results show that the characteristics of road crashes and the severity of injuries depend jointly, i.e., in multivariate (pattern) terms, on gender, age, type of collisions and offences. There are relevant multivariate driver behavior differences in both the type of collisions (and therefore their severity) and the type and number of offences with regard to gender and age of the driver. This research unveils different multivariate driver behavior patterns, providing information about their relative importance (proportion), which helps in road policy decision making in terms of development of prevention measures. The results help in decision making through a potentially better allocation of resources as carried out by road safety regulating offices such as the Spanish Traffic General Directorate (Direccion General de Trafico, DGT).
Highlights
In the literature, extracting vehicle collision patterns among different groups of drivers mainly concerning gender, age, both combined and regarding to driver offences, type of collisions or injury severity, has been a purpose of many researchers in recent years.In the past few decades, the presence of women on the road has increased notably compared to men drivers [1]–[4], and [5]
The main aim of this research is to extract the maximum information in the 8 dimensional multivariable space about the drivers analyzed regarding the aforementioned factors
The ‘‘hard’’ Self-Organizing Maps (SOM) approach applied here can be described within a ‘‘descriptive statistics’’ framework which can be highly sophisticated, as it is stated in well-known multivariate analysis references such as [40]
Summary
In the literature, extracting vehicle collision patterns among different groups of drivers mainly concerning gender, age, both combined and regarding to driver offences, type of collisions or injury severity, has been a purpose of many researchers in recent years.In the past few decades, the presence of women on the road has increased notably compared to men drivers [1]–[4], and [5]. The number of female drivers involved in vehicle collisions has increased in this period [2], [4], and [5]. Men have higher crash rates and greater exposure on the road than females [1], [7], [10], and [11]. Notwithstanding, exceptions to this generalization have been found, such as those observed by [12], which is striking given the use of the number of miles traveled in the denominator of the rate, versus other exposure measures
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