Abstract
Geometric road specifications are defined according to a balance between several criteria.
Thus, safety, vehicles features and economic parameters are taken into account. These criteria are based on theoretic, experimental and statistical knowledge about the influence of different parameters on accident risk.
Geometric design influence, combined with transitory parameters such as weather and traffic conditions, cause different safety limits on each road transverse section.
The methodology for accident risk prediction is based on significance analysis of objective variables, which are involved in different traffic conditions. These objective variables are determined from road geometric characteristics and vehicles features. Besides, and for considering these vehicles features, a vehicle type representative of the car fleet has been defined.
The variables considered in this study are measured in real-time by the CANE vehicle, while it goes along the analysed route at an speed of 80 km/h approximately. This vehicle, developed by
INSIA, has been equipped with on board sensors for the measurement and acquisition of the identified road variables.
Later, these variables are statistically processed by SPSS software for generating a model for the accident risk prediction. Finally, the developed methodology will be applied for predicting the accident risk probability in curves of multilane undivided highways.