Risk of mid-air collision in a lateral plane

Mid-air collision in air transportation is one of the most dangerous safety catego-ries. The risk of mid-air collision assessment is an important component of avia-tion safety estimation. Due to the low number of accidents happened, risk of mid-air collision within limited airspace may be estimated by evaluation of its main components. Paper is more focused on assessing the risk of air traffic separation lost in lateral plane based on air traffic deep learning within predefined airspace. Statistical analysis of current air traffic data and geometrical configuration of routes network are used for probability distribution function fitting. Position of airspace users is obtained from location reports coded by Automatic Dependent Surveillance-Broadcast data format, which is received by ground-based software defined radio. Risk of separation lost in the lateral plane is estimated based on density probability distribution function of airplane unintentional deviations. Fi-nally, the risk of a mid-air collision in the lateral plane is estimated by Reich for-mula for Ukrainian airspace