Mining relationships among knowledge, attitude, and practice of drivers using self-organizing map and decision tree: The case of Bandar Abbas city taxi drivers

Abstract

Background and Objectives: Traffic accidents are the leading causes of fatal or nonfatal work-related injuries in many countries. Analyzing influencing factors on knowledge, attitude, and practice of drivers is a topic of interest for policymakers to decrease traffic accident injury victims. Materials and Methods: In this article, a two-stage data mining approach was presented for determining the mining relationships among knowledge, attitude, and practice of drivers. In the first stage, because of existing multidimensional practice variables, self-organizing map neural network was utilized to automatically arrange drivers into two safe and unsafe driving practice clusters. In the second stage, a decision tree was used to model relationships among knowledge and attitude of drivers and practice clusters. The authors' designed questionnaires were used to collect data in 235 male taxi drivers of Bandar Abbas city in Iran regarding the drivers' knowledge and attitude toward traffic regulations. The driving practices were assessed using a prepared checklist. Results: The most important attribute affecting practice of drivers was the maximum safe speed in the city. Conclusions: The results of this investigation showed that drivers' knowledge toward traffic regulations had a dramatic impact on safe driving practices. Levels of drivers' education can influence practice of drivers.