A Study of a Method for Predicting the Risk of Crossing-Collisions at Intersection

The probability or risk of traffic accidents must be estimated quantitatively in order to implement effective traffic safety measures. In this study, various statistical data and probability theory were used to examine a method for predicting the risk of crossing-collisions, representing a typical type of accident at intersections in Japan. Crossing-collisions are caused by a variety of factors, including the road geometry and traffic environment at intersections and the awareness and intentions of the drivers of the striking and struck vehicles. Bayes' theorem was applied to find the accident probability of each factor separately. Specifically, the probability of various factors being present at the time of a crossing-collision was estimated on the basis of traffic accident data and observation survey data. Accident probabilities were then estimated and compared for different types of intersections, driving patterns of striking vehicles and types of struck vehicles (automobile, motorcycle or bicycle).

The risk of a crossing-collision was found to be high at unsignalized intersections when the striking vehicle is cruising at a steady speed and the struck vehicle is a motorcycle or a bicycle. The results suggest that this combination of factors should be given priority when implementing traffic safety measures. Moreover, this paper also shows that estimated accident probabilities can be used to estimate the accident reduction effect of a driver-support system with warning and information presentation capabilities when the system induces changes in driver behavior.