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Analysis of the Effects of Communication and Surveillance Facility Service Outages on Traffic Separations

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  • Sherry S. Borener
  • Vitaly S. Guzhva

Abstract

This study examines air traffic separations in the service volumes of communication and surveillance facilities that experienced service outages. The data sample consists of 338 unscheduled service outages that happened in 2010 and 2011 at facilities located in the vicinity of 15 major traffic hubs. For each outage, radar track data were collected and used to calculate traffic separations during the period of 30 minutes before to 30 minutes after an outage. Then, the separation index, which indicates the percentage of horizontal separation retained between two aircraft at the same altitude, was estimated. The separation index and loss of separation events were analyzed using lognormal and negative binomial regression models. The results suggest that the count of separation events peaks during the 15 minutes after an outage. In addition, traffic collision avoidance system resolution advisory (TCAS RA) encounters and Category A separation events are 1.31 times more likely during the 30 minutes following the beginning of a service outage, as compared to the 30 minutes before the outage, for both types of facilities. Also, the separation index values are 19% lower following a surveillance facility outage and 4% lower following a communication facility service loss. This study provides evidence that unscheduled service outages of air traffic management facilities are associated with lost or reduced traffic separations and thus can be considered precursors to hazardous loss of separation events.

Suggested Citation

  • Sherry S. Borener & Vitaly S. Guzhva, 2014. "Analysis of the Effects of Communication and Surveillance Facility Service Outages on Traffic Separations," Risk Analysis, John Wiley & Sons, vol. 34(9), pages 1753-1762, September.
    • Handle: RePEc:wly:riskan:v:34:y:2014:i:9:p:1753-1762
    DOI: 10.1111/risa.12192
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    1. Daniel B. Hall, 2000. "Zero-Inflated Poisson and Binomial Regression with Random Effects: A Case Study," Biometrics, The International Biometric Society, vol. 56(4), pages 1030-1039, December.
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