Identifying risky driving behavior: a field study using instrumented vehicles

Abstract

Road crashes continue to be a leading cause of death globally, with most of these crashes reportedly occurring due to human factors. Traditional road safety assessment utilises geometric and traffic parameters that consider road design inadequacies and identify traffic conflicts. However, previous studies do not represent risky driving behavior and its influence on crash occurrence. Incorporating human factors into safety evaluation is crucial to enhance the prediction and subsequent prevention of unsafe events. This research establishes a methodology to identify risky driving behavior using driving performance measures. These measures are computed based on continuous driving profiles collected using instrumented vehicles from a sample set of drivers on an expressway and are compared with historical crash data. The results indicate the significance of driving performance measures in evaluating road safety. The performance measures find application in collision avoidance systems, assessing the road design quality, testing safety countermeasures and guide for policymakers.