John H. Mott

Leveraging Aircraft Avionics for Fleet and Airport Management

Airport operations count data are used for a variety of purposes ranging from allocation of FAA Airport Improvement Program funding to environmental assessments and budget justification. Operations counts are difficult to obtain at small, nontowered airports, which constitute the majority of airports in the United States, and counts frequently are estimated unscientifically by airport managers. Current acoustic and video counting technology is limited, but with the FAA NextGen mandate for installation of automatic dependent surveillance–broadcast (ADS-B) out transponders by January 1, 2020, transponder data are emerging as a viable data source for other than traditional NextGen applications. The NextGen literature has focused on the use of this technology for navigation, safety, and airspace management. This paper introduces a method of applying ADS-B data to fleet management and airport operations. With a 1,090-MHz receiver and appropriate signal processing hardware and software, Mode S and Mode S extended data can be used to track runway operations and fleet usage accurately and cost-effectively. This paper reviews the development of a low-cost portable hardware device and algorithm for obtaining both airport operations count and fleet utilization data. Approximately 1.1 million records collected from sites adjacent to the Purdue University Airport, Indianapolis (Indiana) International Airport, and Paris Charles de Gaulle International Airport are used to provide several examples of airport operations and fleet utilization reports.

Estimation of Aircraft Operations at Airports Using Mode-C Signal Strength Information

An understanding of aircraft operations counts at airports is important due to the use of that information in the allocation of the Federal Airport Improvement Program funds. Operations at airports lacking full-time personnel are typically sampled over time. The sample sizes are generally small, due to the inherent difficulty and expense of deploying acoustic or pneumatic counting devices for extended periods of time, leading to inaccuracies in the estimation of long-term totals. A means of counting operations using Mode S extended squitter (ES) aircraft transponder data received with a 1090-MHz software-defined radio (SDR) system has been developed by the authors. A limitation of this approach, however, is the relatively small ratio of Mode S ES signals to Mode S short squit and Mode C signals, which do not contain aircraft position information. This article presents a method for estimating distance information from the latter two types of transponder signals, enabling them to be used by the SDR-based device, along with Mode S ES signals, in the operations counting process. The distance estimation method described here is based on an adaptive Kalman filter incorporating parameter optimization using known distance information from Mode S ES signals and results in an average error of 0.83 nm in measurements within a 5-nm radius of the receiver. This level of uncertainty enables the use of Mode S short squit and Mode C signals to count aircraft operations at airports without the additional overhead of multilateration.

Estimation of aircraft distances using transponder signal strength information

Abstract The Federal Aviation Administration has recently mandated the installation of transponders that provide position reporting (Extended Mode S) in aircraft operating in most types of domestic controlled airspace by January 1, 2020. The resulting proliferation of aircraft transponder data has accelerated the potential for the use of such data in measuring operations counts at nontowered airports, as it may be easily collected with an inexpensive receiver and analyzed with appropriate algorithms. While many of the data (Basic Mode S and Mode C) do not include aircraft position information, the portion of Extended Mode S data that do may be used to directly compute the distance of the corresponding aircraft from a receiver located at an airport of interest. This article describes a method by which these computed distances may be utilized to calibrate an adaptive digital filter that can subsequently estimate distances for the remainder of the transponder records that do not provide position information. The digital filter is a combined first-order Butterworth low-pass filter and a Rayleigh maximum likelihood estimator for the signal variance. The resulting distance estimates from two different antenna installations exhibited median absolute deviations of 0.92 and 1.12 nm per transponder record, respectively, within 5.0 nm of the receiver. These accuracies are sufficient for the estimation of aircraft operations counts at nontowered airports.

Accuracy Assessment of Aircraft Transponder–Based Devices for Measuring Airport Operations

Accurate counts of aircraft operations at unmonitored or partially monitored general aviation airports are difficult to achieve, but they are important because of their effect on the allocation of federal and state airport improvement funds. Impediments to correctly counting aircraft operations include inaccuracies related to the acoustic counters that are commonly used to collect data and errors in the statistical procedures that extrapolate the sample data into meaningful counts. In response to these impediments, the authors developed a measurement technique that uses data from aircraft transponders to determine the occurrence of aircraft operations at these airports. To validate the accuracy of this technique, operations counts calculated from its use at a general aviation airport in the state of Indiana were compared with those obtained from the FAA’s Air Traffic Activity Data System database, which contains official operations data reported by airports with towers. This comparison, which was conducted using data for April 2016, indicated that the new technology provided values within 5% of the 7,837 total operations reported by tower operators. The transponder signal–counting technology thus appears to be an effective and inexpensive means of establishing accurate operations counts not only at these airports but potentially at the more than 2,800 of the 3,331 airports in the National Plan of Integrated Airport Systems that lack associated air traffic control towers.

Estimation of aircraft operations at airports using nontraditional statistical approaches

The FAA annually invests approximately

The Detection and Minimization of Cheating During Concurrent Online Assessments Using Statistical Methods

3B in small commercial and general aviation airports, with additional infrastructure investments appropriated at the state level. Accurate airport operations counts are critical for fair and efficient allocation of federal and state funds for airport development and improvement. Of the 3,331 airports in the United States that constitute the National Plan of Integrated Airport Systems, however, only slightly more than 500 have either full-or part-time air traffic control facilities and associated personnel who are available to manually register those counts. There are several methods used to count aircraft operations at airports lacking full-time personnel; these are generally based on traditional statistical sampling techniques. Sample data is typically obtained by employing short-term contract staff to deploy acoustic and pneumatic counting devices and to provide human observations. The sample sizes associated with these methods are inherently small due to financial constraints. Small sample sizes create difficulties in terms of estimation of the population mean and variance from the sample parameters because the normality assumption of the distribution of the sample means may not hold. Two modifications to the estimation procedure are suggested here. The first employs a Frequentist model based on sampling without replacement from a discrete, finite, uniformly-distributed population. The second involves a Monte Carlo simulation and associated Bayesian hierarchical model using a Poisson likelihood function, which incorporates the inherent Poisson nature of the underlying arrival process and assumes uncertainties in the registration of operations counts. The latter approach is shown to improve substantially the accuracy of both the traditional, unmodified predictor and the Frequentist modification.