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ATSC 231 Aviation Meteorology

Published : 26-Aug,2021  |  Views : 10

Question:

Topic

Runway Incursion Avoidance

Research Question

Have the current technologies in use by aircraft to avoid runway incursions decreased the rate of actual documented runway incursions?

Hypothesis

Ha: The current anti-incursion technology used by airports has reduced the number of runaway incursion.

Ho: The current anti-incursion technology used by airports does not change the number of runaway incursion.

Answer:

Introduction

The anti-incursion technologies have been able to look at the incursion problem from different angles and always trying to address the issue. Some of the key technologies which have been introduced in the industry to curb the increase of incursions include Airport Surface Detection Equipment model X (ASDE-X), Final Approach Runway Occupancy Signal (FAROS), and Runway Status Lights (RWSL) (FAA, 2014). In United States, the Federal Aviation Administration (FAA) has been able to develop these technologies with an aim of reducing the runway incursions. This part of the paper will research on the performance of the anti-incursion technologies and the performance of these technologies to reduce the occurrence of the incursions. Data on the number of incursions happening will be collected from different sources. First the credibility and valid of data will be looked. Data analysis tools will be used in this section in order to lead to proper conclusion of the research question. These technologies have been introduced to guide the pilots and other airport users. The guidance is done to ensure that the incursion does not happen. But in most cases, the incursions do happen and the analysis of the events and occurrences of the runway incursions is able to show how effective the technologies are (Wilke, Majumdar, & Ochieng, 2015). This paper will analyze the occurrence of incursions between 2013 and 2015. This paper will give an overview of the technologies and the way they have been able to function on areas where they have been introduced. Additionally, this paper will look at details from FAA on the number of runaway incursions which has been able to happen within these years.

Research Design and Approach

This section will be able to review data from the number of source on the way incursions have been able to happen. The research will rely on quantitative information from FAA website, NTSB’s and NASA’s databases. The data will involve the number of runway incursions which has been able to happen over the past. According to FAA database (2015), the data below is able to show the number of incursions which have been able to happen between 2013 and 2015.

The following data is able to show the way the incursion trends have been able to happen between 2013 and 2015. The table shows the different types of incursions and the way they have occurred on this period. It has to be noted that the technologies were function and the incursion occurrence kept increasing instead to reducing. The total number of incursions is also increasing per year even with the use of technologies to reduce the incursions.

Year/incursion type

Operational incursion

Other incursions

Pilot deviations

Vehicle/pedestrian deviations

Total

2013

243

4

783

212

1242

2014

258

8

764

234

1264

2015

323

2

881

252

1458

Table 1: Table of number of incursions per year between 2013 and 2015 when anti-incursion technologies were implemented

The research will analyze these information and presenting graphical representations in the analysis. In addition, information from other sources will be analyzed different and used for comparison to ensure that the data is accurate and valid. The use of the technologies has been able to play a key role in ensuring that the aviation sector is safer. All the types of runway incursions keep on increasing under the period of consideration even with this measure to reduce the runway incursions. According to the FAA details, in 2015, at least one incursion was able to happen per day (FAA, 2014b). The increase usage of airports seem to be causing more incursions each year. The above data is able to show that the number of incursions keeps on increasing in the aviation sector even with the implementation of the technologies by the FAA (FAA, 2014a). The FAA announced that in 2015, it is going to invest about $11 million in the anti-incursion technologies to control the runway incursions. A quick review is able to show that the technologies are performing below the expectations in reducing the runway incursions.  

Setting and Sample

According to FAA the ASDE-X, FAROS, and RWSL are implemented to reduce the amount of runway incursions which are able to happen on the airports. This analysis looked at key ten airports in different states in United States, where either of the technologies have been implemented. Details about runway incursions from the ten airports were collected and analyzed in terms of the incursions in each of the airports. In addition, the number of runway incursions in each airport was compared to the passenger traffic in the specific airports. This was able to the analysis and coming up with the conclusion on the effect of the passenger traffic to the number of runway incursions available (Cozza, & Young, 2013). Through this analysis, I was able to focus on details of the incursions which were attained from these 10 airports in different states with the anti-incursion technology. The analysis was carried from 2013 to 2015. The analysis shows that the increase of passenger traffic leads to an increase on runways incursions which occur on those airports. The analysis showed that lack of the technology presence did not inhibit occurrence of the incursions. To some extent, the technologies were unable to control the number of runway incursions happening on the airports. The following table is able to show the 10 airports which their details were used for analysis of the same specific incursions. The details of incursion before installation of the anti-incursion technology and after the installation of the technology are clear shown.

Rank

Airport Name

Passenger

Traffic (million)

Incursion between 2001 and 2008

Incursion between 2013 and 2015

city

1

Hartsfield-Jackson Atlanta International Airport

50.48

61

40

Atlanta

2

Los Angeles International Airport

39.64

64

44

Los Angeles

3

O'Hare International Airport

37.50

66

20

Chicago

4

Dallas/Fort Worth International Airport

31.27

47

13

Dallas/Fort Worth

5

John F. Kennedy International Airport

29.22

40

24

New York

6

Denver International Airport

28.25

48

30

Denver

7

San Francisco International Airport

25.71

36

19

San Francisco

8

McCarran International Airport

22.67

39

21

Las Vegas

9

Charlotte Douglas International Airport

21.96

31

19

Charlotte

10

Seattle–Tacoma International Airport

21.89

45

26

Seattle/Tacoma

Table 2: Relating passenger traffic in airports and incursions in table 1

Addressing Bias

Each and every research has its own part of biasness whether it is from the source of data to be analyzed or from the analysis way of the data. In this research, I sought to establish whether the technologies are helping to reduce the runway incursion experienced in airports of not (FAA, 2012). A look at the number of runways incursions which happened from 2013 to 2015 as well as the effect to 10 busiest airports in USA was done in this paper (Air Traffic Activity System (ATADS), 2014). In addition; I was able to acquire data from other channels such as NTSB’s and NASA’s databases for comparison purpose. This helped to come up with better analysis of the data acquired through comparing my point of view with other people analysis of the same data. The data acquired from all these sources was the one which FAA has published for public view. The multiple data sets were done in order to reduce the biasness of data acquisition and analysis.

In order to achieve the best analysis, researcher used quantitative data and related it to the effect felt from the airports point of view. The data was acquired from the FAA details on the runways incursions experienced on those years. Throughout the analysis, I was able to maintain independence of data processing and data collection. This was helping me to keep openness regarding the outcome which I could achieve and introduction of biasness. This factor helped to reduce any biasness which I could introduce to the data analysis and acquisition.

Data Collection and Analysis– I have coved this section.( do not write this)

Data Collection

The data for this research study will be collected from primary sources. These data will not divide in to qualitative variables such as pilot gender (male / female), race (white, Hispanic, etc.) or the aircraft fleet (B777, A330, A321,etc.). Instead of dividing in to qualitative variables will be separated into two main groups that determines those that agree that change is required, and those that disagree. To accomplish this study, the researcher will gather and organized data will be quantitative. This primary data source includes pre-existing data retrieved from other organizations and previous research studies conducted in this context. Moreover, crucial data will be collected from NTSB’s and NASA’s databases to determine the inferential relationship between various factors that are pertinent to the runway incursions.  Further data on ground vehicle technology equipment for drivers and pedestrians, existing flight technology for pilots, communication systems can be obtained to investigate if the technology limitation is a potential cause of these human-bound runaway incursions.  These data will also obtain from pre-existing data will be collected from primary sources.  Identifying these potential factors will help the researcher to develop the hypothesis.

Tabulating Data

The data will be tabulated using computer software. The sample data obtained for the research study can be appropriately analyzed and will presented by using Microsoft excel (Dale, 2001). MS Excel extensively used for the entry and data management and it has a large range of statistical functions that are very advantageous.  Moreover, the tables creating by excel can be compare different variables column wise, row wise or values either chart or table format. The data will be presented in the APA-style format.

Graphical representation of Data

The data will be plotted to display the graphical characteristic and parameters of the data. For effective analysis and better representation, histograms and plots can be used to graphically demonstrate the obtained data. Therefore, the researcher will be using StatCrunchdata analysis software to determine the shape of the distribution and to be applied on row data. StatCrunch delivers an extensive array of numerical and graphical routines for analyzing data with numerous features can be used for pedagogical purpose. These graphical characteristics and graphs will be represented in APA-style format.

Understanding the Sampling Distribution

The statistical method to test the hypothesis will be determined based on the shape and graphical characteristic of the graph. To determine the obtained data is parametric or non-parametric used in StatCrunch histograms and the plots will be very essential. For an instant to determine the data is parametric using histogram, it is essential that the shape of the frequency histogram is identical shape of a bell-shaped curve and if not, it will be non-parametric. After identifying the normality of data which determined using through StatCrunch, the decision will be made on which appropriate statistical hypothesis method to be used to test the hypothesis.

As further described by James and Sood (2006), the different methods of hypotheses test like the z-score, t-test, ANOVA and Chi-square can only be employed if the sample data has a systematical distribution (i.e. is normally distribute and is not skewed to the left/right). For this study researcher, will decide which hypotheses test to be used based on the shape of the data.

Using appropriate statistical method

The researcher will complete the analysis of the data using an appropriate statistical method. For this research study, a significant level of 5% (i.e. p=0.05) can be used. The significance level state the acceptance of result to a margin of 5% for committing a Type-I error. In line with this, a 95% confidence interval will be used to describe the statistics for a broader population in concern.  

i.e. the p-value for the hypothesis testing will be =0.05.

As mentioned on the research question, runway incursions avoidance data could be built on statistics hypothesis to determine the precise factors for each case of incident or incident conditions and the possible technology to eliminate such faults (Freguson& Nelson, 2012). The report explored and declared the potential of statistical analysis to provide safer air flights operations through data analysis in the example of runaway incursions avoidance.  The data collected from multiple reports and can support such hypothesis or deny the validity of such assumption to provide a mean of incursions avoidance.

Moreover, as mentioned above to test the hypothesis concern is depend on the results of the statistical analysis. The p value must be > .05. This value designates the extreme threshold of possibility of gaining an exact set of data that the researcher is eager to except, given the null hypothesis is correct.  If a statistical test designates that the probability of obtaining a particular data set was <p value set by the researcher, the null hypothesis is accepted. If the statistical test indicates that the probability of obtaining a particular data set was ≥ p value, the null hypothesis is rejected.

Should the null hypothesis be rejected, the analysis would suggest that the research hypothesis is supported and the conclusion will have a statement similar to the following: “The result of the statistical test of the hypotheses indicates that the null hypothesis can be rejected.  This suggests that the research hypothesis of this Graduate Capstone Paper is supported.  The result of the analysis is statistically significant at the p=0.05 value or 95% confidence interval”. On the other hand, if the null hypothesis is not rejected, plausible explanations will be provided as to why the null hypothesis was not rejected.

Validity and Reliability

Achieving credibility and accuracy is important for any research. The importance of credibility is able to enhance coming up with viable conclusion. The FAA website updated its data in quarterly basis and this shows that valid and accurate data is always updated. The presence of the data before and after the technologies were installed on the different airports is still present on the FAA website. The presence of the data from different periods is able to show that the data is well stored and accurate and therefore can be used for research. Checking for the validity of the data was another key step which is had to take in the process of conducting this research. As noted by Air Line Pilots Association, International. (2007), the validity of the analysis measures whether the researcher is measuring what he or she is required to measure. The data analysis which I did was able to originate from the key source as required and chose the key data to analyze the issue.

Updating the data at different periods is a key process to show the accuracy and credibility of data source. In addition, in order to determine the validity and credibility of the research, the data acquired from FAA was compared from other sources (FAA, 2015). Critical thinking on the similarity of the data from the different sources was compared. In addition, the annual data was compared with the different quarterly data which was available in FAA website. Cumulating the data was able to prove the credibility and validity of the annual data presented. Through these numerous aspects, the data used on this research was assumed to be valid and credible.

Summary

Use of technologies to address the increase of incursions has been a key goal of FAA. The introduction of the anti-incursion technologies has happened since 2002. Nevertheless, the incursion increase has ever been experienced even with the implementation of these technologies. The use of aviation sector has become a key section of travel for many people, leading to increase of congestion on aviation stations. This increase had led to an increase on the number of runway incursions occurring. Although the technologies have been implemented to reduce the runway incursions, the opposite has been experienced year in year out. Quantitative data was retrieved from FAA website, NTSB’s and NASA’s databases in order to enhance the analysis. In addition, in order to enhance the credibility of the data source, the selection of the databases and sources of data was closely selected. The use of the different systems and databases was able to ensure that different data was compared in order to make proper conclusion. This chapter collected quantitative data from different airports on the incursion. The data collected included incursions occurring on different airports in US before the installation of the anti-incursion technologies and also data of incursions after the installation of the technologies. The data spans are able to show that the anti-incursion technologies have not worked well in the reduction of such accidents and incidents in the airports.

References

Air Line Pilots Association, International. (2007).White paper-runway incursions: call for action. Retrieved from http://www.alpa.org/portals/alpa/runwaysafety/runwayincursionwhitepaper.pdf

Air Traffic Activity System (ATADS). (2014).Airport operations[Datafile]. Retrieved from http://aspm.faa.gov/opsnet/sys/Airport.asp

Cozza, B G., & Young, J P. (2013).Runway incursions: A case study analysis. Retrieved from http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1020&context=atgrads  

Federal Aviation Administration (FAA). (2012).Runway safety report2013–2015. Retrieved from http://www.faa.gov/airports/runway_safety/publications/media/2012-AJS-475-FY2011-Runway-Safety-Annual-Report.pdf

Federal Aviation Administration (FAA). (2014a).Aviation safety information analysis and sharing system for runway incursions (ASIAS)[Datafile]. Retrieved from https://pdfs.semanticscholar.org/5f48/d5502469004f1fc850d18bc7c826b345f7b7.pdf 

Federal Aviation Administration (FAA). (2014b).Commercial service airports, based on calendar year 2013 enplanements[Data file].Retrieved from http://www.faa.gov/airports/planning_capacity/passenger_allcargo_stats/passenger/media/cy13-commercial-service-enplanements.pdf

Federal Aviation Administration (FAA). (2015).Runway safety runway incursions. Retrieved from http://www.faa.gov/airports/runway_safety/news/runway_incursions/

https://www.faa.gov/airports/runway_safety/statistics/year/?fy1=2014&fy2=2013

Wilke, S., Majumdar, A., & Ochieng, W. Y. (2015). The impact of airport characteristics on airport surface accidents and incidents.Journal ofSafety Research,53, 63–75.

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