Deterministic and Probabilistic Conflict Detection Algorithms for NextGen Airport Surface Operations

The paper deals with the development a ground-side conflict detection automation system for NextGen airport surface operations. The automation system is referred to as “Monitor Airport Environment: Surface Traffic and Runway Operations (MAESTRO).” In contrast to current-day conflict detection systems, MAESTRO has been designed taking into account NextGen operational concepts from mid-term and far-term timeframes. Conflicts of interest are Taxiway Collisions and Runway Incursions. A new conflict alert referred to as “Runway Incursion Situation Alert (RISA)” is created to actively prevent runway incursions. The automation system is driven by surveillance inputs and the outputs from airport planning systems such as Spot and Runway Departure Advisor (SARDA). MAESTRO consists of three modules: (i) Trajectory Prediction module, (ii) Conflict Detection module, and (iii) Controller Display module. The trajectory prediction module generates the 4D-trajectory predictions along with their uncertainty estimates. The paper develops the framework for both deterministic and probabilistic conflict detection. MEASTRO has been tested using actual surface traffic data from Dallas/Fort Worth International Airport (DFW). The evaluations indicate promising performance with zero missed-alerts and few false alarms that are actually close encounters. It is shown that situations which could potentially become Runway Incursions could be detected as RISAs with a lead-time of 60 seconds.