Margaret McNamara

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.

Performance Ranking of Arterial Corridors Using Travel Time and Travel Time Reliability Metrics

Performance measures are essential for managing transportation systems and demonstrating agency accountability. Probe vehicles are an effective means for gathering vast amounts of information about highway networks. This paper presents a scalable methodology for analyzing arterial travel times that considers both the central tendency and the reliability of the travel time. A pilot analysis was carried out for 28 arterials with a total of 341 signalized intersections across Indiana. Starting from individual minute-by-minute speed records, the data were converted into travel times and aggregated into time series cohorts that correspond to typical traffic signal time-of-day periods. The data were normalized for the ideal travel time (based on the speed limits on each route) to account for individual route lengths and speeds. The data were compiled for all Wednesdays from January through July 2014 for investigation of arterial characteristics. The results show that a greater density of traffic signals on a route loosely corresponds to higher average travel times and less reliability. A composite index incorporating both the average values and reliability characteristics of travel time is developed and is used to rank the arterials by performance.

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

Assessment of Speed Limit Locations Using Crowdsourced Probe Vehicle Data

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.

Real-Time Probe Data Dashboards for Interstate Performance Monitoring During Winter Weather and Incidents

Recently there has been an increased interest in using technology to apply variable speed limit techniques to Interstate roadways. An important component of changing speed limit signs (or variable speed limit signs) is an outcome assessment that collects measured data for understanding the effect on the network. For 265 mi of I-65 in Indiana, the speed limit locations were geolocated and paired with crowdsourced probe vehicle data to develop metrics that engineers and decision makers could use to optimize speed limit placement. Four case studies show the effect of different types of speed reduction and opportunities to improve the current placement. The visual performance measures described in this paper are readily deployable and scalable to any freeway in the country for assessing variable speed limits, work zone speed limits, and static speed limits.

2013–2014 Indiana Mobility Report: Full Version

The Indiana Department of Transportation (INDOT) manages over 1800 centerline miles of interstate that can be profoundly impacted by weather, crashes, and construction. Real-time performance measurement of interstate speeds is critical for successful traffic operations management. Agency managers and Traffic Management Center decision makers need situational awareness of the network and the ability to identify irregularities at a glance in order to manage resources and respond to media queries. One way to access this level of detail is crowdsourced probe vehicle data. Crowdsourced probe vehicle data can be obtained by collecting speed data from cell phones and global positioning system (GPS) devices. In Indiana, approximately 2673 predefined interstate segments are used to generate over 3.8 million speed records per day. These data can be overwhelming without efficient procedures to reduce and aggregate both spatially and temporally. This paper introduces a spatial and temporal aggregation model and an accompanying real-time dashboard to characterize the current and past congestion history of interstate roadways. The primary high level view of the aggregated data resembles a stock ticker and is called the “Congestion Ticker.” The data archive allows for after-action review of major events such as ice storms, major crashes, and construction work zones. The utility of this application is demonstrated with two case studies: a snowstorm that covered northern and central Indiana in February 2015 and an I-70 back of queue crash in April 2015.