This paper considers the errors that arise in using outdated accident prediction models in road safety scheme evaluation. Methods to correct for regression-to-mean (RTM) effects in scheme evaluation normally rely on the use of accident prediction models. However, because accident risk tends to decline over time, such models tend to become outdated and the estimated treatment effect is then exag...

background: injuries and fatalities from road traffic injuries are global public health concerns, and a major problem in the iran. this study aimed to explore strategies to control road traffic injuries (rti) in iran. methods: we conducted a qualitative study to explore possible ways to reduce the occurrence of road traffic injuries in iran in 2016. interviewees were purposively sampled from va...

This paper presents an evaluation of the effects on road safety of new urban arterial roads in Oslo, Norway, and a synthesis of evidence from similar studies that have evaluated the safety effects of new urban arterial roads in other cities. A before-and-after study was made of four urban arterial road projects in Oslo. The study controlled for general accident trends in Oslo and for regression...

An approach for automatic road extraction from digital aerial imagery is presented. The extraction is based on a semantic model for roads. The images are divided into different so-called “global contexts”: rural, forest, and urban. Different parts of the road model and different strategies are used in the different global contexts. In rural areas, a multi-scale approach is employed to find init...

Road traffic accidents (RTAs) are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury; Ethiopia in particular experiences the highest rate of such accidents. Thus, methods to reduce accident severity are of great interest to traffic agencies and the ...

This paper presents a semantic case-based reasoning framework for text categorization. Text categorization is the task of classifying text documents under predefined categories. Accidentology is our application field and the goal of our framework is to classify documents describing real road accidents under predefined road accident prototypes, which also are described by text documents. Acciden...

This paper presents a vision based road boundary detection based on the combination of dynamic programming and 2nd order polynomial approximation. The initial estimate of the most likely road boundaries are obtained by a modified dynamic programming on a cost field defined on the edge image. A second order polynomial is fit to the initial coarse estimate of road boundaries to obtain a refined e...