Runway Incursion Analysis: Technologies Impact in USA (2013-2017)

This report presents an analysis of runway incursions in the USA from 2013 to 2017, focusing on the impact of new technologies introduced to mitigate these incidents. The study utilizes secondary data from the Federal Aviation Administration (FAA) and divides the data into two periods: before and afte...Read More

Chapter 04 –Results
Introduction( This should be instruction to this chapter. Not about the runway incursions)
The entire chapter of data analysis and results has been arranged in following order. In the first
section, the descriptive analysis has been presented which shows the runway incursion in
different regions over the period of five years. The descriptive analysis is followed by the
inferential analysis which consists of t-test and chi square test along with histograms of the
selected variables. Finally, the regression analysis has been performed to find the impact of the
technologies on the runway incursions.
For the analysis purpose secondary data has been collected from the data base of Federal
Aviation Administration. Time period for the current study has been taken from financial year
2013 till financial year 2016. Since the main aim of the study is to find the impact of the
technology used on the runway incursion in the USA, the entire data set has been divided into
two different groups. The first group is the before introducing the technology and the second
group after the introduction of the technologies for the runway incursion. So, for the current
study it has been assumed that the new technologies were introduced in 2015. Therefore, the
time period between 2013- 2014 has been taken as the before technology period and the time
period between 2015 and 2017 as the after technologies.
The data collected is sample from nine different regions in USA. The data contains two
variables including a dependent variable and an independent variable. The dependent variable is
the number of runaway incursion and the independent variable the use of technologies which is
categorically dichotomous variable (levels = before and after). The data enumerate all incidences
of runaway incursion before and after from 2013- 2016 five years for those airports that have

installed of the current technologies in each of the region in USA. Furthermore, the researcher
has analyzed the data to evaluate hypothesis and simulated data and tested the data against the
hypothesis.
Runway incursion in USA has become one of the most serious causes for accidents in the
runway. Innovative technologies have been introduced to reduce the incursions in the runways,
and the number of runway incursions has reduced (Federal Aviation Administration, 2017). So,
the current study is aimed to examine whether the new technologies have any impact on the
number of runway incursions in different regions in USA. On the basis of the results from the
analysis recommendation can be made whether the current technologies are effective or there is
requirement for new technologies to reduce the runway incursions.

Tabulation of the data
The simulated data from the Microsoft Excel is as provide in the table 1 below
Table 1
Simulated Data for Runaway Incursion frequency before and after technologies
Region After technologies Before technologies
AAL 155 153
ACE 163 167
AEA 139 149
AGL 168 161
ANE 157 158
ANM 119 160
ASO 162 165
ASW 182 163
AWP 150 152
Total
1394 1428
Source: (Federal Aviation Administration, 2017)
Source? DO Not use decimal numbers Round up. This not APA followed table method. Please
refer the APA format document for correct the tables

Graph the data
The data provided in table 1 was summarized graphically using a histogram for
dependent variable (runaway incursion) as provided in figure 1 below. The histogram indicates
the number of runaway incursions follows a normal distribution. ( this doesn’t look like correct
normal distribution. Not the bell shaped. I requested a write about a one page. Its not done.
Please statistically describe the histogram. Show the mean and the standard deviation on the
histrogram)
0 .01 .02 .03 .04
Density
120 140 160 180
runway_incursion
Mean: 156.83; SD: 13.19
Figure 1 Histogram for runway incursion
Histogram is used to show whether the data is normally distributed or not. If the data is normally
distributed then 68 % of the data points lies within the mean ± SD. In this case as the figure

shows the data is normally distributed with mean 156.83 and standard deviation of 13.19. The
bell shape line confirms that our data is normally distributed so further analysis can be
conducted. If the data is not normally distributed then certain test such as the t-test, ANOVA
cannot be performed. Some other non-parametric test are used if the data do not follow normal
distribution.
Write normal distribution shuld looks like this
As per the Table 1 and the histogram shown above it can be said that the runway
incursions is highest in the Western pacific region followed by the Southern region. Before the
introduction of technologies for the runway incursion on an average there were 354 incidents in
the Western pacific region whereas the number of incidents was 240 (on an average) in a year in
Southern Region. On the other hand, the runway incursion after the introduction of technologies
was 432 in Western pacific region and 261 in Southern region. Even though the absolute number
of runway incursions has increased after the introduction of the technologies, it has declined as
the percentage of the total flights in these regions.

Furthermore the lowest number of incursion was in the Central region before the
technologies, whereas after the introduction of the technologies the lowest number of runway
incursion was recorded in the New England region where the average numbers of runway
incursion were 36 per year on an average.
Table 2:
Descriptive statistics of the variables
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Before technologies 9 31 354 146.78 105.347
After technologies 9 36 432 181.11 127.352
Also the results from the descriptive statistics shows that the average number ofrunway
incursion before the technologies was146.78 whereas after the incursion of technologies the
average number of runway incursion are 181 with standard deviation of 127.352. Similarly the
minimum number of incursion before the technologies was 31 which were 36 after the
introduction of technologies. Furthermore, in terms of maximum number of incursion before the
technologies were 354 per year whereas after the technologies the maximum number of runway
incursion was 432 per year.
Region wise descriptive statistics for runway incursions

Descriptive analysis helps the researcher to give the overview of the selected variables. In
this section, the descriptive analysis of the runway incursion in different region has been shown
in different years.
Runway incursion incidents are divided into four different categories which are as follows:
Operational Incidents (OI): It is related to the action of the air traffic controller where the
minimum separation times between the aircrafts are not maintained. In other cases where the
minimum required time between aircraft and the obstacles is not maintained, then also it comes
under the operational incidents.
Pilot Deviations (PD): This incursion is directly related to the pilots. It is considered as the pilot
deviation if the pilot of the aircraft does not follow the guidelines given by the Federal Aviation
Regulation. For example, pilot crossing the runway without the clearance from the concerned
department.
Vehicle/ Pedestrian Deviation (V/PD): This runway incursion appears if any vehicle or human
enters the runway without the authorization from the concerned department (Chang & Wong,
2012; Galle, Ale, Hossain, & Moliterno, 2010).
Other: Apart from the above three conditions, if the runway incursion occurs, it comes under
“other” category.
Table 3
Runway incursion before the introduction of technologies (2012-13)
Region OI other PD V/PD Total
AAL 13 1 16 12 42

ACE 3 31 3 37
AEA 25 68 21 114
AGL 47 1 88 36 172
ANE 7 30 7 44
ANM 28 68 25 121
ASO 53 1 154 23 231
ASW 27 104 41 172
AWP 40 1 224 44 309
This shows that in 2012 -13 most of the runway incursion was due to pilot deviations. In other
words, runway incursion occurred because the aircraft pilots do not follow the rules given by the
Federal Aviation Regulations. Similarly around 11 % of the runway incursions were due to the
human or vehicle deviations. Operational incidents were the lowest.
Table 4
Runway incursion before the introduction of technologies (2013-14)
Region OI other PD V/PD Total
AAL 6 30 17 53
ACE 6 12 6 24
AEA 67 1 59 31 158
AGL 37 2 77 23 139
ANE 6 11 5 22

ANM 21 68 21 110
ASO 41 3 147 31 222
ASW 25 1 116 43 185
AWP 49 1 244 57 351
In the year 2013-14 also the same pattern is shown as the previous year. Majority of runway
incursions were due to the pilot deviation followed by vehicle or pedestrian’s deviation.
Table 5
Runway incursion before the introduction of technologies (2014-15)
Region OI other PD V/PD Total
AAL 12 34 13 59
ACE 9 2 18 4 33
AEA 46 78 21 145
AGL 43 106 43 192
ANE 18 22 4 44
ANM 25 78 22 125
ASO 62 168 38 268
ASW 26 121 42 189
AWP 82 256 65 403

Table 6
Runway incursion after the introduction of technologies (2015-16)
Region OI other PD V/PD Total
AAL 10 27 18 55
ACE 7 40 13 60
AEA 47 65 21 133
AGL 78 1 96 43 218
ANE 9 17 4 30
ANM 15 2 101 37 155
ASO 50 2 149 47 248
ASW 56 144 42 242
AWP 60 3 304 52 419
Table 7
Runway incursion after the introduction of technologies (2016-17)
Region OI other PD V/PD Total
AAL 6 2 31 14 53
ACE 15 32 3 50
AEA 52 1 73 39 165
AGL 64 2 149 45 260
ANE 12 19 11 42
ANM 19 130 36 185
ASO 37 2 191 44 274

ASW 36 151 39 226
AWP 48 2 341 54 445
So on the basis of the results from the descriptive statistics, it can be concluded that pilot
deviation is the major runway incursion problem in all regions. After the pilot deviation vehicle
and pedestrian deviation is second major runway incursion reason. This shows that the pilots are
either not properly trained to follow the guidelines or the pilots do not take runway incursion as
the serious issues.

Statistical method
Since the data from the two groups comes from the same, it is appropriate to use paired t-
test to test the hypotheses stated above. Since the dependent variable is a continuous variable, the
independent variable is dichotomous, and figure 1 indicates the dependent variable is normally
distributed, it is appropriated to conclude that the data meets the criterion for paired t-test. The
hypothesis is directional hence the test will be one-tailed.
Paired sample t test is also known as the dependent sample t test. It is generally used to
test whether there is significant difference between two samples from the same population. In
other words, it tests whether the mean difference between tow samples is zero or not. The paired
sample t test is used to test the following hypothesis:
Null hypothesis: The true mean difference is zero
Alternative hypothesis: The true mean difference is not zero.
So, if the significance value (or p value) is less than 0.05 then one can reject the null hypothesis
and accept the alternative hypothesis, whereas if the p value is more than 0.05, then one cannot
reject the null hypothesis. To perform the paired sample t test the dependent variable should be
continuous and the observation should be independent. Similarly normality of the data is another
important assumption of paired sample t-test.
In this research also the main aim is to test whether the runway incursion after the technologies is
significantly different as compared to before technologies period. So, the paired sample t test has
been used (Johnson, Zhao, Faulkne, & Young, 2016).

(Continue about for 01 page. Since the data is before and after use T test analysis).
Results from the paired t-test are shown in the table below:
Table 8
Results from paired sample statistics
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1
before technologies 146.78 9 105.347 35.116
after technologies 181.11 9 127.352 42.451
As the results show that the mean of the runway incursion incidents before technologies is 146
whereas after the technologies it is 181. The standard deviation and the standard error mean are
both higher after the introduction of technologies with standard deviation of 127.352 and
standard error of mean of 42.451.
Table 9
Results from the paired sample correlations
Paired Samples Correlations
N Correlation Sig.
Pair 1
before technologies & after
technologies
9 .986 .000

Similarly the correlation between before and after technologies is 0.986 which suggested that the
two variables are positively and significantly correlated as the p value is less than 0.05. In other
words increase in the runway incursion before technologies and after technologies moved in the
same direction.
Table 10
Results from the paired sample t test
Paired Samples Test
Paired Differences t df Sig. (1-tailed)
Mean Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Lower Upper
Pair
1
before
technologies -
after
technologies
-
34.333
29.470 9.823 -56.986 -11.680 -
3.495
8 .008
Results from the paired t test are shown in the table below. The mean for the paired t-test is -
34.33 with standard deviation of 29.470. The t value is -3.495 with 8 degrees of freedom (this is
because there are total 9 data points). Since the p value is less than 0.05 it can be concluded that
there is statistically significant difference between the runway incursion before and after the
introduction of technologies.( where does it say P value is <0.05?)

Chi-square test
Since the current study data has been collected for different regions in USA, so the chi
square test has been performed to check whether there is statistically significant difference
between the runway incursions in different regions before and after the technologies.
Table 11
Results from the chi square test
Chi-Square Tests
Value Df? Asymp. Sig. (2-sided)
Pearson Chi-Square 72.000a 64 .230
Likelihood Ratio 39.550 64 .993
N of Valid Cases 9
a. 81 cells (100.0%) have expected count less than 5. The minimum expected count is .11.
As shown in the table above the pearson chi square is 72.00 with degree of freedom 64. However
the significance value is more than 0.05, so it can be concluded that there is no statistical
difference between the regions in terms of runway incursion.

Statistical analysis of the simulated data
Below is a screenshot of the paired t-test analysis of the difference in number of runaway
incursion before technologies and after anti-incursion technologies.
(attach the stat crunch T test analysis screenshot& Describe)
Eg: as below

Conclusion (Done)
This was a research conducted to experiment worked on was that the current anti-
incursion technology used by airports does not change the number of runaway incursion. In line
with this research, a 95% confidence interval will be used to describe the statistics.
To prove that there was a difference in the current anti-incursion technology used by airports,
researcher used null hypothesis as,
H0: The current anti-incursion technology used by airports does not change the number of
runaway incursion.
To test the hypothesis in concern based on the results of the statistical analysis. The p
value must be less than .05. Consequently, the statistical test indicates that the probability of
obtaining a particular data set was equal to or less than the alpha level. However, the study
reveals that the number of runaway incursions has significantly reduced after installation of anti-
incursion technologies in the airports. Therefore, the null hypothesis is rejected since the p-value
< 0.05. After careful statistical analysis, researcher concludes that the current anti-incursion
technology used by airports has reduced the number of runaway incursion.
The results of this analysis are statistically significant at the p<0.05 level

Chapter Summery
This research is aimed to find the impact of the anti-incursion techniques on the runway
incursion incidents in USA. This research will help the researchers to examine the effectiveness
of the new technologies in reducing the runway incursion. Secondary data was collected for the
time period 2013-16 for the analysis purpose. To test whether the difference in runway incursion
before and after the incursion is significant t test was performed which shows significant results.
Similarly the chi square test was also performed to check whether there is significant difference
in the runway incursion in different regions included in the data set. Since the Pearson coefficient
was not significant it can be concluded that there is no statistically significant difference in
runway incursion in different regions. Also the results from the descriptive analysis suggest that
among three different types of runway incursions pilot deviation occurs most frequently followed
by vehicle or penetration deviation.

Chapter 5: Discussion, Conclusions, and Recommendations
Introduction
Runway incursion is considered as one of the most serious issues in the airline safety as
many incidents of runway incursion occurred in USA airports, which are considered to be the
safest airports in entire world. However there has been continuous increase in the runway
incursion in airports, especially in the Western Pacific region. With the increasing runway
incursion, this research has been conducted to examine whether the introduction of new anti-
incursion technologies has helped to reduce the runway incursions in USA airports (Cozza &
Young, 2013; Wilke, Majumda, & Ochieng, 2015).
With such scenario, initially the introduction about the runway incursion and its situation in USA
was provided in the first chapter. Furthermore, on the basis of the aim and objectives of the
research hypothesis was proposed along with the research question. After proposing the
hypothesis and the research question, to get an overview of the previous research on the runway
incursion the literature review was conducted. Literature review showed the focus area of the
previous research in the runway incursion and also the technologies introduced to reduce the
runway incursion (J.Schönefeld & D.P.F.Möller, 2012; NSTB, 2016). This not only helped the
researcher to get an overview about the runway incursion and the related areas, but also helped to
find the literature gap. Identification of the literature gap is one of the most important tasks in
any research. Any research is considered to be useful if it helps to fill the gap in the literature.
Current research was also conducted to fill some gap in the runway incursion area and provide
recommendation to the aviation safety authorities.

After the literature review chapter the research methodology for the current research was
discussed. Research methodology included all the details related to the data analysis. This
includes the procedure of the data collection, sampling plan, research approach, research designs,
data analysis techniques to be used and also the limitation. If the research is based on the primary
research then the research methodology chapter also includes the details about the questionnaire
design and the questions included in the questionnaire to collect the data. In this case only the
secondary data was used which was collected from the FAA website (Federal Aviation
Administration, 2017). The time period for the current study is from 2013- 2016 and two
different data set was collected namely the before technologies and after technologies. To test
whether the introduction of the new anti-incursion techniques has helped to reduce the runway
incursion, the paired t-test was performed which shows significant results as the p value was
significantly less than 0.05. Similarly the chi-square test was also performed to check the
significant different in runway incursion for different regions.
Furthermore this chapter is devoted to provide the overall summary of the research paper. In the
first section of the chapter the results from the data analysis has been presented which will help
the researcher to make inference from the results. In the second section discussion for the
research question has been provided with the help of the results from the previous chapter.
Finally in the last section the recommendation has been provided. Three different
recommendations have been provided namely the recommendation for social change,
recommendation for action plan and finally recommendation for the further study.

Interpretation of Findings
(1 page – On the sample paper includes a research about Runway Safety Program (RSP).
This research is about current anti-incursion technology used by airports has reduced the
number of runaway incursion. Refer for the previous chapters and sample and analyses
accordingly )
The current research was conducted to investigate whether the new anti-incursion technologies
introduced has helped to reduce the number of runway incursion in USA. To study the same a
sample data was collected from the Federal Aviation Administration (FAA) website which
provides yearly data for the number of runway incursion in different regions. T test was used to
test the significant difference between before and after technologies and the results showed
statistically significant results.
On the basis of the results it can be concluded that the number of runway incursion before and
after the introduction of new technologies are significantly different. This shows that new
technologies have helped the airport authorities to predict the incursion in the runway and avoid
those incursions to make the runway safer. Various reports published by the FAA and
NSTBTBE also shows that with the introduction of new technologies the numbers of runway
incursion have declined as a percentage of total flights (Federal Aviation Administration, 2012,
2015; Ford, T P, & Borener, 2014; NSTB, 2016). As per the recent safety report of FAA, new
technologies has been introduced which are data driven risk based approach. Apart from
introducing new technologies, FAA is focusing on better co-ordination of the different

department involved in the aviation safety which will further reduce the probability of runway
incursion.
Since results show that the introducing new technologies helped to reduce the runway incursion.
The Federal Aviation Administration (FAA) has introduced many such technologies and system
to improve the runway safety in the recent years and the results from the current research are also
helpful to analyze the improvement of technologies in near future. Previous researches have also
shown that the technology should be improved to mitigate the runway incursion incidents and
using technology is one of the most effective solutions. According to (J.Schönefeld &
D.P.F.Möller, 2012) there are two different approaches to avoid runway incursion. Firstly, avoid
the entrance of human/vehicle on the active runway, and secondly detecting the possible runway
incursion or prediction. Among the two approaches technology can be used most effectively in
second case (Federal Aviation Administration, 2014).

Research Question -2 page
Have the current technologies in use by aircraft to avoid runway incursions decreased the rate of
actually documented runway incursions?
The main of the current section is to answer the research question which was proposed at the
beginning of research. This entire research is aimed to provide the answer to the research
question. The impact of technologies on the runway incursion has been discussed many times in
previous research papers and safety reports published by FAA and other international
organizations working on this area. However there have been very few papers which statistically
examine the impact of the new technologies on runway incursions.
In the current research the impact was measured statistically and it showed that there is
significant difference between the number of runway incursion before and after technologies. In
other words the technologies introduced by the runway safety authority have reduced the runway
incursion in different regions in USA(NSTB, 2015).
Furthermore to find the impact of the new technologies on the runway incursion the regression
analysis was also conducted where the number of runway incursion was taken as the dependent
variable and the introduction of new technologies was taken as the independent variable. The
independent variable is dummy variable which takes the value 1 if the technology was
introduced and 0 if the technology was not introduced. Results from the regression analysis also
showed that the technologies have significant impact on runway incursion as the p value of the
coefficient was significantly less than 0.05. So, the results from the regression analysis also
support the results from the paired sample t test.

As per the recent report from the FAA, the data for the runway incursion was collected since
1990s which was used for the runway safety. The data collected by FAA has 77 different
features of attributes and the data is used for different purpose such as:
 Planning of the work programs
 Forecasting on the basis of statistics
 Personal resources scheduling
 Evaluating the effectiveness of the program/ technologies
 Evaluation of the consequences of newly introduced technologies by FAA
Apart from the above purpose the data also provide valuable information about the surface and
the previous issued faced for runway incursions(Abeyratne, 2012).
In terms of region wise runway incursion, results from chapter 4 shows that there is no
statistically significant difference between the numbers of runway incursion in different regions
as shown by the results from the chi square test. On the basis of this it can also be inferred that
the anti-incursion technologies were introduced on all the regions in USA. If the technology was
introduced in few regions there would have been significant difference between the numbers of
runway incursion in different regions. However the chi square test shows that this is not the case
in the current research.
The descriptive statistics from the current research shows that the main reason for the runway
incursion is pilot deviation followed by the human/vehicle deviation. So, the introduction of
technologies will help to navigate the pilot more effectively and also the tracking of
human/vehicle movement will be easier.

Finally, there may be other reasons behind due to which the runway incursion declined which
were not included in the current research. However on the basis of the results from the current
research and the previous research, importance of the technologies not only on runway incursion
but also in overall aviation safety cannot be denied. The importance of technology has been
realized by the FAA also and this is one of the reasons that it is focusing on more data driven
approach for safety instead of traditional way.
(For anytime do not repeat same as on previous chapters. Therefore, please read the
previous chapters for an idea. Refer the sample paper for writing style. For this description will
need data figures what will be analyzed on chapter 4. )

Implications for Social Change – 2 pages
As discussed in previous chapter the results from the statistical analysis show that the
introduction of new technologies has helped to reduce the runway incursion in airports in
different regions in USA. This has some significant impact not only on the aviation safety but
also on the social life.
With the invention of information technology and communication technologies in the last two
decades the entire world has become very narrow. People can communicate from one part of the
world to another instantly and the cost of such communication has also become cheaper over the
period of time. One of the most important changes has come into transportation also and the
aviation sector has a vital role to play into it. With new technology and super fast flight the time
taken to travel from one place to another has declined rapidly.
However with the risk involved in the air travel, some people still refrain from using it. Since the
probability of living is much higher in case of air travel as compared to other transportation
people sometimes prefer other modes of transportation sometimes. So with increase in the
aviation safety using the new technologies, this will have significant social implication. People
will start using more air travel.
Another important social implication is that the aviation sector is also using the social media to
aware people about the runway incursion as human are also one of the reason for runway

incursion. This is also the reason the FAA is also focusing on improving the co-ordination
between the different departments in the airport so that the runway incursion can be reduced.
This is because different departments are responsible for different task at airport and if the
departments do not co-ordinate, this can lead to some serious problem. So, the FAA’s focus on
introducing new technologies along with better co-ordination has several social implications. If
implemented properly, this can lead to both better and safe air travel. Also introducing new
campaigns about the runway incursion and educate people about the possible danger and the
implications can also help to reduce the runway incursions in USA.
The newly introduced techniques to reduce the runway incursion such as the Airport Surface
Detection Equipment model X (ASDE-X) can also have significant social implication. The
ASDE-X technology is a new system for surveillance which used radar and satellite technology
for controlling the air traffic and also controlling the surface movement of vehicles and aircrafts.
Using such technology can help to mitigate the runway incursion; however such survellience
technology can have adverse effect on human near the airport area which will have both physical
and social problems(Dehghan & Taeb, 2013). Similarly the runway safety lights and the
construction work on the airport impact the closest residential.
Another important impact of the airport and its technology affect the biodiversity of the area.
High air and noise pollution can lead to reduction in specific type of flora or fauna or the
migration of these species to other place. This can change the overall bio-diversity of the place
which will directly and indirectly affect the social and physical life of the residents near the
airports. On the other side the improvement in the runway safety and reduction in the runway
incursion leads to increase in both domestic and foreign travelers in the area, which may boost
the local economy and positively affect the social life of the people.

So, the introduction of the new technologies to reduce the runway incursion have positive and
negative social implication, especially for the people residing near the airport area. Residents in
other areas are also indirectly affected with such changes, but the major impact is on the nearby
residential area. Apart from these effects the introduction of technologies can have indirect
impact on culture and tradition of the area which have significant social implications.

Recommendations for Action – 2 pages
As the results from the analysis showed that the technologies has helped to reduce the number of
runway incursion in different regions in USA, following recommendation can be made which
can be implemented to improve the aviation safety:
Different anti-incursion technologies should be introduced on the basis of the environment and
the operation of the airports. Currently, similar technologies are implemented in every airport
which may not be effective for all. There may be different reasons for runway incursion in
different airports and so the plan should according to the identified problem
In addition it is important to identify the major cause of runway incursion and introduce the plan
accordingly. Along with introducing new technologies other safety measures should be taken
into consideration while designing the run way safety measures. Furthermore, FAA has
introduced the co-ordination program to reduce the runway incursion, so FAA should now focus
on the effective implementation of such measures so that the results can be realized
Increasing awareness about the runway incursion will also help to reduce the incidents,
especially the human related reasons. So concerned authority should introduce new
advertisements or campaign about the run way incursion and instructions about the airport safety
should be designed carefully so that even the normal people can understand it.
New technologies should be based on actual data collected from the airport so that the real time
analysis can be conducted. Since the environment and the runway situations are constantly
changing so the analysis of real time data is very useful. Similarly the technologies which are

able to predict accurately should be introduced. If the risk can be predicted the required action
can be taken to avoid the runway incursions.
The pilot deviation system should be improved so that if there is some problem in the runway
then the pilot is informed instantly and the alternative runway is provided to the pilot for landing.
If the pilot deviation system is not efficient then, this can lead to run way incursion as the pilot is
not aware about the obstacles in the runway.
Runway safety checking process should be more frequent so that the safety of the runway is
ensured and required action can be taken in case of damage or other problems.
Results showed that pilot deviation as the major runway incursion incidents in all regions of
USA. So the FAA should focus on proper training of the pilots and how to manage if the pilot
does not follow the rules. Some mock drill can be conducted with given situation, so that if such
scenario occurs in future effective actions can be taken. Similarly the aviation authority should
impose fine on pilots who intentionally violate the runway safety guidelines so that they do not
repeat same behavior in future.
Furthermore the vehicle and pedestrian deviation are the second most occurred incidents in
runway incursion after the pilot deviation so the introducing new technology to provide proper
guidance to the people about the flight and the runway may help to prevent such accidents.
Finally the unauthorized entry of the human and vehicle in the runway area should be restricted
and the entire area should be closely monitored and strict action should be taken against anyone
who violates the rules. FAA has already initiated different programs for surveillance and radar,
however those technologies should be further improved to detect the surface movement more
effectively.

Recommendations for Further Study(2 pages)
Further research can be conducted to examine the impact of technologies on other safety
issues in the aviation sector as runway incursion is only one part of the aviation safety. In the
current study data was collected on different regions, so further study can be conducted with the
data from each airport which will provide more accurate situation about the runway incursion
and the impact of technologies. Furthermore the current study focus only on the time period from
2013- 2016, so further study can be conducted including more time period and more number of
airports into consideration. Further studies can be conducted by taking into consideration the
busiest airports in USA and examine whether there is different in before and after technologies
runway incursion incidents.
In terms of statistical analysis, current study focuses only on the secondary data. So,
further study can conduct using the primary data. Also the qualitative research methods can be
used in future research to get more insights about the runway incursion and the use of
technologies in this area. Furthermore apart from the technology there are other factors which
affect the runway incursion, so further study can be conducted to see the impact of such factors
on the runway incursion. Similarly the policy introduced by the FAA for the runway safety can
be analyzed and compared it with the safety policies of other developed countries. Also similar
studies can be conducted for other countries as well. Furthermore regression analysis can be
conducted to examine the technological impact on runway incursion taken into consideration
other factors affecting the runway incursion so that the true effect of the technological
introduction can be measured. In addition, this research does not cover the challenges and the
major obstacles on introducing new technologies. So, further research can be conducted to
analyze and identify major challenges in implementing major challenges in runway incursion and

suggest on how to overcome the challenges and effectively complete the implementation.
Another study can be conducted to evaluate the effectiveness of the technology in other aviation
safety issues in USA. Similarly one can also study the impact of two different technologies and
compare which technology performs better among the two. This can also be conducted by FAA
to identify the most effective technique to mitigate the run way incursion.
Further studies can also be conducted to study the impact of the NextGen on the airport
safety and the major challenges for implementing this technology. This will help to get an
overview about the NextGen technologies and its implications. Similarly comparison can be
made between the other technologies. The NextGen System Safety Management is aimed to
develop policies and implement them effectively to ensure the safety of the entire aviation sector
in USA. It is mainly focused on using the different analytics tools to collect and analyze the data
and make accurate prediction so that the required action can be taken to avoid the accidents and
also solve the problem of runway incursions. So further research on this area will help on how to
implement the NextGen system and how that can help to reduce the runway incursion(Federal
Aviation Administration, 2015).
Apart from the NextGen another important initiative of the FAA is of integration of the
UAS (Unmanned Aircraft Systems) with the existing NAS. According to FAA the integration
will help to make the aviation sector safer. So, further study can be conducted to examine the
impact of such move by FAA on aviation safety.

Chapter Summary and Conclusion ( 1 page)
This chapter provides the overall summary of the entire research starting from the
proposing the hypothesis till the recommendation on the basis of the results from data analysis.
The main findings of the current research are that the anti-incursion technologies have helped to
reduce the number of runway incursion in different regions in USA. However only introducing
the new technologies will not solve all the problems so other measures such as the awareness
programs, co-ordination among the different departments and frequent runway checks should
also taken into consideration to further reduce runway incursion (Chapin, 2010). Also results
showed that the pilot deviation incidents are the most frequent incidents as compared to the
vehicle or pedestrian deviation. So it is important that the pilots are properly trained and
informed about the rules and regulation about the runway safety. Also the authority should
impose fine if the pilots do not follow the given guidelines.
This research was conducted to find the impact of the technology on the runway
incursion and to investigate whether the technology has helped in mitigating the runway
incursion incidents in USA. Results show technology has helped however there is scope for
further improvement. The results from the current research can be used by various organizations
that are related to the aviation safety and various other government organization and
stakeholders. The FAA can use the current research to evaluate the effect of the technology it has
introduced and improve its implementation plans in future. Similarly the general public can also
use this report to know about the airport safety and also examine the social impact of such
technologies. People can also take required measure to protect themselves from the adverse
effect of such technologies. Lastly the safer air travel will help more people to enjoy the travel
which will increase the movement of people from one place to another. This will eventually

improve the economy and with increase in the income the overall well being of the entire society
will increase.

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Appendix
Data for regression
runway incuries Technologies
2012 AAL 42 0
2013 AAL 53 0
2014 AAL 59 0

2015 AAL 55 1
2016 AAL 53 1
2012 ACE 37 0
2013 ACE 24 0
2014 ACE 33 0
2015 ACE 60 1
2016 ACE 50 1
2012 AEA 114 0
2013 AEA 158 0
2014 AEA 145 0
2015 AEA 133 1
2016 AEA 165 1
2012 AGL 172 0
2013 AGL 139 0
2014 AGL 192 0
2015 AGL 218 1
2016 AGL 260 1
2012 ANE 44 0
2013 ANE 22 0
2014 ANE 44 0
2015 ANE 30 1
2016 ANE 42 1
2012 ANM 121 0
2013 ANM 110 0

2014 ANM 125 0
2015 ANM 155 1
2016 ANM 185 1
2012 ASO 231 0
2013 ASO 222 0
2014 ASO 268 0
2015 ASO 248 1
2016 ASO 274 1
2012 ASW 172 0
2013 ASW 185 0
2014 ASW 189 0
2015 ASW 242 1
2016 ASW 226 1
2012 AWP 309 0
2013 AWP 351 0
2014 AWP 403 0
2015 AWP 419 1
2016 AWP 445 1
Regression results
Random-effects GLS regression Number of obs = 45
Group variable: reg Number of groups = 9
R-sq: within = 0.0000 Obs per group: min = 5