Validation of New Technology Using Legacy Metrics: Examination of SURF-IA Alerting for Runway Incursion Incidents

Journal of Aviation Technology and Engineering

Joslin, Robert¹ 

[1] Embry-Riddle Aeronautical University

关键词: runway incursion;    Automatic Dependent Surveillance-Broadcast;    Runway Incursion Severity Classification;    Enhanced Traffic Situational Awareness on the Airport Surface with Indications;    Alerts;   

DOI  :  10.7771/2159-6670.1096

学科分类：社会科学、人文和艺术（综合）

来源: Purdue University

【 摘 要 】

This study demonstrated an innovative method of utilizing expert raters and actual high-risk incidents to identify shortcomings of using legacy metrics to measure the effectiveness of new technology designed to mitigate hazardous incidents. Expert raters were used to validate the Enhanced Traffic Situational Awareness on the Airport Surface with Indications and Alerts (SURF-IA) model for providing alerts to pilots to reduce the occurrence of pilot deviation type runway incursion incidents categorized as serious (Category A or B) by the legacy FAA/ICAO Runway Incursion Severity Classification (RISC) model. The study concluded that the SURF-IA model did not yield an outcome of a Warning or Caution alert for all pilot deviation type runway incursion incidents classified as serious by the FAA/ICAO RISC model. The different outcomes between the RISC and SURF-IA models may result in misleading information when using the reduction in serious runway incursion incidents as a metric for the benefit of SURF-IA technology.

【 授权许可】

Free