







































Copyright © CC-BY-NC 2019, BJMSR 

 

Bangladesh Journal of Multidisciplinary Scientific Research; Vol. 1, No. 2 
ISSN 2687-850X      E-ISSN 2687-8518  

July-September; 2019 
Published by Centre for Research on Islamic Banking & Finance and Business  

 

     54 

Causal Factors of Flight Delay in Nigerian Airport: A Case Study of Murtala 
Muhammed International Airport 

 
 

Ayo-Agunbiade Oluwafisayo T. 
Department of Transport Management Technology 

Federal University of Technology Akure, Nigeria 
E-mail: ayofisayo90@gmail.com 

 
 

Stephens Mobolaji S. 
Department of Transport Management Technology 

Federal University of Technology Akure, Nigeria 
E-mail: msstephens@futa.edu.ng 

 
 
Abstract 
Delay is a prominent event mostly experienced at the airport by both airline and passengers. Globally delay is permitted at 15mins 
later than the scheduled time. Assessing the level of delay in the airport is a major criterion for measuring airport performance. 
Aircraft not adhering to schedule flight results in increased airport congestion, cancellation flight and flight delay. This study made 
use of primary data through well-structured questionnaires to obtain information from selected NAMA Staff at the Murtala 
Muhammed International Airport (MMIA). Operational delay report for a month and Airlines flight were examined for two 
months under the MMA2 to know the deviation of scheduled flight time from actual flight time. Descriptive Analysis, Wilcoxon 
Rank test, Factor Analysis and Step Wise Regression were the statistical tools deployed for this research. Findings showed that on 
a monthly base, 63% of the domestic traffic flows are delayed. Some factors responsible for delay were considered: aircraft damage, 
bad weather, aircraft maintenance, VIP movement and fueling are the major factor responsible for delay of aircraft Movement. 
 
Keywords: Flight Delay, Factor Analysis, Congestion, Scheduling.  
 
1. Introduction  
Flight planning requires accurate weather forecasts, enhances safety regulations, whereby adequate or excess fuel are carried beyond 
the minimum needed to fly from origin to destination (Simpson, Bashroum and Carr, 1965). Scheduling process is where many 
of the implications that stem from the earlier work come together. The scheduler allocates each aircraft to each service, taking into 
account:  turnaround time; the minimum time needed to empty and refill an aircraft when it passes through an airport where 
aircraft will be overnight, as not all aircraft will necessarily be at the hub on every night of the week. With each schedule, the airline 
defines its daily operations and commits its resources to satisfying its customers’ air travel needs. Therefore, one of the basic 
requirements all airlines place on the ground handling is to ensure high efficiency of handling activities, avoiding delays. This 
approach of a flight schedule will reduce delay and improve the operation of the airport. Jianfeng (2012) observed that a delayed 
air flight which might have occurred out of positive and negative circumstances tends to have resultant effects on the passengers, 
airlines and airport running costs and on other flights arrivals and departures. Lack of adherence to the schedule designed by 
airport results in increased airport costs, congestion, flight delays and cancellations will have medium and long term reduced 
patronage effects, fewer passenger will want to fly through such airports as well as fewer airliners will want to fly airport prone to 
disruptions. Some flights are also affected by reactionary delays, due to late arrival of previous flight. 

Flight delays not only annoy air travelers and disrupt their schedules, but they incur costs to the airlines when flight 
connections are missed, or flight crews and aircraft need to be reallocated due to maintenance problems or crew duty time limits. 
Flight delay can have subsequent effect on those on board and the operation of the flight and other scheduled flight at an arrival 
and departure airport. Aircraft schedule recovery and crew re-assignment, taking into account all constraints, factors and 
regulations, while trying to find solutions in a few minutes. The problem is to keep the effects of disturbances as low as possible 
and to return to regular operations. (Mueller & Chattigi, 2002).  

Many major airports around the world have significant delay problems as a result of an imbalance between capacities 
and demand. Delay often results in the increase in fuel consumption of aircrafts, affects passengers getting to their destination on 
time (Boye, 2015). When traffic occurs in bunches or peaks, there may be delays even when the number of aircraft using the 
airport is less than the capacity for that peak time period. Some amount of delay arises every time two aircraft are scheduled to use 

mailto:ayofisayo90@gmail.com
mailto:msstephens@futa.edu.ng


 Copyright © CC-BY-NC 2019, BJMSR 

 

 www.cribfb.com/journal/index.php/BJMSR       Bangladesh Journal of Multidisciplinary Scientific Research         Vol. 1, No. 2; 2019 

55 
               

a runway at the same time. The probability of simultaneous arrivals increases rapidly with traffic density, so that average delay per 
aircraft increases exponentially well before traffic levels reach capacity levels. Having considered all these, there are various factors 
instituting delay in airline operation such as the relationship of demand to available capacity of an airport causes delays to aircraft 
movements as a result of limited runway capacity. 

In developing economy like Nigeria, delay experienced by passengers is a point of consideration in the development of 
a nation as a whole as it affects personal expenses. Developing countries need to focus on the delay of aircraft movement at their 
airport to ensure increased patronage by airliners. 

In summary, an accurate overview of the literature related to aircraft flight scheduling and delay has been x-ray by building 
upon the sections addressing this topic in the literature reviews by Mueller and Chatterji (2002), the effects of not adhering to 
schedules are increased airport costs, congestion, cancellation of flights and flight delay. Studies such as Mohleji (2001), Schaefer 
and Miller (2011), Hansen (2002), Mueller and Chatterji (2002) and Rosen (2002)) on airport congestion have identified several 
factors which generate flight delays: saturation of airport capacity (including air transportation control activities), airline problems, 
reactionary delays, passengers and cargo, weather and other unpredictable disruptions (e.g. strikes). 

This research considered the major factor(s) responsible for delay of scheduled flight by considering the capacities 
utilizations, runway usage, and deviations in departures and arrivals. Grigoriy (2014), opines that there is acceptable level of flight 
delay, which might have positive effect on the overall operation of the airport system. The study was on flight delay performance 
at Hartsfield Jackson Atlanta International Airport with objective of determine the delay at the airport. This research was carried 
out at Hartsfield Jackson Atlanta International Airport and not Muritala Mohammed Nigeria Airport Nigeria. The researchers 
conducted a field based qualitative research and quantitative research, based at Murtala Mohammed Airport, in other to analyse 
the delay of aircraft at the airport. The various possible factors responsible for delay were also addressed. For the purpose of this 
study the MMIA is chosen as the case study. This is because this airport serves main aviation hub in the Nigerian aviation industry 
for domestic and international traffic (cargo and passengers). 

The aim of the paper is to analyze aircraft delay of selected airline at the airport. A case study of Muritala Muhammed 
International Airport, Lagos, Nigeria. To achieve this aim, the following objectives are delineated as to estimate the average rate 
of deviation of actual arrivals, and departures from scheduled arrivals and departures; and evaluate the factors causing delay of 
scheduled flights. 

 
2. Theoretical Framework  
2.1 Time Space Analysis 
Time space analysis is a simple technique to assess runway and airspace capacity if the headway between aircraft is known. It is a 
method to ascertain the time complexity and space complexity of an algorithm. Time complexity is a measurement of how much 
computational time an algorithm uses as its input size changes. Analyzing algorithms in this way provides an indicator as to how 
quickly the runtime increases, how it does so in relation to the input and weather the algorithm is costly for small/large inputs 
(Trani, 2013). 
 
2.2 The Basic Queuing Process 
Queuing theory is concerned with the issue of waiting; it is important to note that waiting can be quite boring, hence queuing 
theory examines how customers (vehicles) arrive to receive service by servers which is between arrivals of vehicles, start of service, 
and wait in queue. The basic applications of queue are: 

▪ Number of customers in queue L (for length); 
▪ Time spent in queue W for (wait) (Adeniran and Kanyio, 2019). 

The basic process assumed by most queuing models is the following. Customers (Passengers) requiring service are 
generated over time by an input source. These customers (Passengers) enter the queuing system and join a queue. At certain times, 
a member of the queue is selected for service by some rule known as the queue discipline. The required service is then performed 
for the customer by the service mechanism, after which the customer leaves the queuing system (Fredick, 2001). Queues are called 
infinite or finite, according to whether this number is infinite or finite.  

The assumption of an infinite queue is the standard one for most queuing models, even for situations where there actually 
is a (relatively large) finite upper bound on the permissible number of customers, because dealing with such an upper bound would 
be a complicating factor in the analysis. At a high level, we can view this problem from the perspective of queuing theory. Delays 
are fundamentally related to one of several issues: (a) higher scheduled demand than capacity or over-scheduling (b) a larger arrival 
rate than scheduled, (c) a lower service rate than scheduled, or (d) a smaller number of servers than scheduled. In the context of 
this paper, the gates are the servers and the service rate are the rate that aircraft can be turned at the gate. For example, if an aircraft 
has a longer turnaround time than scheduled, this is effectively a reduction in the service rate of the gate. 

The model adopted to assess this research is Queue theory, it is the process whereby customer enters a system and service 
is being rendered. For this research the customer is Aircraft (both passengers and freight), the input source is the gate, the queue 
discipline is mostly First Come First Serve, Last in last out and emergency and the size is the total number of the aircraft per day 
for a period of 2 months. 



 Copyright © CC-BY-NC 2019, BJMSR 

 

 www.cribfb.com/journal/index.php/BJMSR       Bangladesh Journal of Multidisciplinary Scientific Research         Vol. 1, No. 2; 2019 

56 
               

2.3 Flight Delay 
Wu (2005) states that flight delay is a complex phenomenon, because it can be due to problems at the origin airport, at the 
destination airport, or during airborne. A combination of these factors often occurs. Delays can sometimes also be attributable to 
airlines. Some flights are affected by reactionary delays, due to late arrival of previous flights. These reactionary delays can be 
aggravated by the schedule operation. Mueller and Chatterji (2002) states the flight schedules are often subjected to irregularity. 
Due to the tight connection among airlines resources, delays could dramatically propagate over time and space unless the proper 
recovery actions are taken. Even if complex, flight delays are nowadays measurable. And there exists some pattern of flight delay 
due to the schedule performance and airline itself (Wu, 2005). Flight delay might be initiated by the airline, when airplane is not 
field to capacity and have might resultant effect on subsequent arrivals. The delay can be attributed to the airport, when there are 
limited ground-handling facilities, with increase demand. Failure of an aircraft to be cleared and failure to take off within 15mins 
of scheduled timing is delay. 

There are general arrival and departure delays. This usually indicates that arrival traffic is doing airborne holding or 
departing traffic is experiencing longer than normal taxi times or holding at the gate. These could be due to a number of reasons, 
including thunderstorms in the area, a high departure demand, or a runway change. In order to understand flight delay, it is useful 
to consider the phenomenon of scheduled delay. The simplest way of reducing delays is not to increase the speed and efficiency 
of the system to meet the scheduled time, but to push back the scheduled time to absorb the system delays (Bai, 2006). 

 
Figure 1. Factors influencing departure delay 

Source: Yufeng, Michael and Wolfgang (2005) 
 

2.4 Disruption Management  
This section of the literature review focuses on the review of some decision support tools. The following papers are approaches 
that aim to aid airline controllers rather than replace them with an automated decision maker. Teodorovic and Stojkovic (1995), 
describe an approach to the operational daily airline scheduling problem. Their research provides airline dispatchers with a 
decision support tool to aid their rules of thumb and enable them to make globally informed recovery decisions. Also their 
approach generates new airline schedules in response to disruptions provides the airline dispatcher with automatically generated 
recovery decisions which can be manually overridden. 
 
3. Method of data collection 
This study relies on secondary data sourced from the Federal Airport Authority of Nigeria (FAAN), Nigeria Airspace Management 
Agency (NAMA) and Nigeria Civil Aviation Authority (NCAA). The data obtained were records of flights over a period of 
sixteen (16) years. To address the first objective, the installed airside capacity, and the hourly and daily traffic movements were 
examined. Daily traffic flow for two months and report of flight delay for a month were obtained. Daily traffic flow, for two 
months was gotten and report of flight delay. Descriptive Analysis, Wilcoxon Rank test, Factor Analysis and step wise regression 
were employed for the analysis. 198 respondents were given questionnaire and 160 were retrieved, the sample size where obtained 
according to Cochran’s Sample Size Formula. For the first objective, the traffic flow of aircraft for Standard at MMA2 was worked 
on, the Standard and Actual time of Arrival and Departure was analysed with the use of Wilcoxon, descriptive analysis, also with 
the aid of Excel Sheet. Secondly, several factors affecting delay where analysed through the use of factor analysis where the factors 
were categorized into three categories. 



 Copyright © CC-BY-NC 2019, BJMSR 

 

 www.cribfb.com/journal/index.php/BJMSR       Bangladesh Journal of Multidisciplinary Scientific Research         Vol. 1, No. 2; 2019 

57 
               

4. Results and Findings 
4.1 Estimation of deviation Arrival and Departure Rate  
Table 1:   Executive summary on international and domestic flight operations  

Routes Number of 
Airlines in 
Operation 

Total Flights 
Operated 

No. of 
Delays 

Flight 
Cancellations 

Remarks 

International 
Airlines 

29 1,478 587 14 Cancelled Flights: Africa World (7), British Airways 
(3), Air Peace (1), Arik Air (1), Asky (1), Egypt (1) 
Air/Ramp Return: Air Peace (1) 

Domestic 
Airlines 

8 5,168 3,244 29 Cancelled Flights: Arik Air (10), Air Peace (7), 
Overland Air (4), Medview Air (3), Aero (2), 
Azman (2), Dana (1) 
Air/Ramp Return: Arik Air (2), Dana Air (1) 

Source: Nigerian Civil Aviation Authority, August 2018 
 

For the period under review, a total number of 1,478 flights were operated on international routes while 5,168 flights 
were operated on domestic routes.  587 flights out of 1478 were delayed for International flight and 3244 out of 5168 flights 
were also delay, so therefore delay is evident for both. 
 
Table 2: Flight delay occurrences 

Table 3: Wilcoxon rank test of difference between group 

Group Observe Rank Sum Expected 

Arrival 8 75.5 68 

Departure 8 60.5 68 

Combined 16 136 136 

Wilcoxon Statistic 0.791 

P value 0.4292 

Source: Authors’ Compilation, 2018 
 

𝑯𝟏: 𝑨𝒓𝒓𝒊𝒗𝒂𝒍𝑫𝒆𝒍𝒂 ≠ 𝑫𝒆𝒑𝒂𝒓𝒕𝒖𝒓𝒆𝑫𝒆𝒍𝒂𝒚 

We reject 𝑯𝟎 if the probability value is less than 𝒑 = 𝟎. 𝟎𝟓. 
 
Since the P value is not less than 0.05, this gives a numerical evidence to conclude that the delay caused by flight departure 

is relatively the same for delay caused by flight arrival. 
It can be seen that flight delay was most experienced on the 29th April to 5th May 2018 during the month of April to 

May. It has been observed that 53.08% of the total flight arrivals experienced delay within the space of two months at 15mins 
beyond the scheduled time. Table 3 shows the weekly occurrence of flight delay in the airport within the period of two months, 
April to May. It can be observed that flight delay is evident and occur often in arrival and departure of aircrafts. This might be 
due to bad weather after take-off. 

 Arrival Departure Total arrival and take-off 

8-14/04/2018 65 58 137 

15-21/04/2018 71 70 155 

22-28/04/2018 85 90 196 

29/04/2018-5/05/2018 81 78 129 

6-7/05/2018 16 25 38 

13-19/05/2018 70 58 117 

20-26/05/2018 83 58 123 

27-30/05/2018 45 28 77 

Total 516/1132 465/1132 972 

Total flight Activity 972  

% Flight delay 53.08% 47.83%  

Source: Murtala Muhammed Airport Terminal 2, 201 



 Copyright © CC-BY-NC 2019, BJMSR 

 

 www.cribfb.com/journal/index.php/BJMSR       Bangladesh Journal of Multidisciplinary Scientific Research         Vol. 1, No. 2; 2019 

58 
               

4.2 Factors Causing Delay 
Factor analysis was conducted on the nine variables using the Principal Component Analysis method of Varimax Rotation with 
Kaismer Normalization. The principal components grouped the factors causing delay in both international and Local Flight by 
ensuring the variables were not inter-correlated and that the variables were grouped properly. 
 
Table 4: Total Variance Explained 

Component Initial Eigenvalues Extraction Sums of Squared Loadings 

Total % of Variance Cumulative % Total % of Variance Cumulative % 

1 4.951 55.016 55.016 4.951 55.016 55.016 

2 1.305 14.502 69.518 1.305 14.502 69.518 

3 1.011 11.237 80.754 1.011 11.237 80.754 

4 .804 8.932 89.686    

5 .439 4.875 94.561    

6 .189 2.103 96.664    

7 .134 1.486 98.150    

8 .121 1.341 99.492    

9 .046 .508 100.000    

Source: Authors’ Compilation, 2018 
 

Table 4. shows the variance in flight delay accounted for by the first three principal components, extracted by principal 
component method using SPSS. It can be seen that about 81% variation causing flight delay was accounted for by three 
components. About 19% of variation was accounted for by the remaining six components. The initial Eigen values are flight delay 
variations accounted for by the entire components, while the Extraction loadings are those variation accounted for by the factors 
extracted. 

 
Table 5: KMO and Bartlett's Test 

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .691 

Bartlett's Test of Sphericity Approx. Chi-Square 395.315 

Df 36 

Sig. .000 

Source: Authors’ Compilation, 2018 
 

Only factors with Eigen values equal to or greater than 1 were considered as significant. The Eigen value of a factor 
represents the amount of the total variance explained by that factor. The results lead to three categories for the factors causing 
delay. 

These factors that causes delay can be attributed to Aircraft course as component one, processing factor as component 
two and customer factor as component three. 
We can generally conclude from the study that delay is evident in the airport and may be as a result from the airplane maintenance, 
delay due to customer arrival or attitude and the method of processing information. 
 
4.3 Step Wise Regression 
Table 6: Step wise Regression 

Model Summary 

Model R R Square Adjusted R Square Std. Error of the Estimate 

1 .379a .143 .081 .727 

2 .378b .143 .088 .724 

3 .378c .143 .095 .722 

4 .370d .137 .096 .721 

5 .361e .131 .097 .721 

6 .353f .124 .097 .721 

Source: Authors’ Compilation, 2018 
 

From table 6, above where the analysis of factor causing delay is represented. Factors with little or no effect are baggage 
handling, aircraft fueling, VIP movement, congestion of airspace and flight documentation. The factor retained by the analysis has 
the most important factors are Aircraft damage, bad weather, aircraft maintenance and late boarding. 



 Copyright © CC-BY-NC 2019, BJMSR 

 

 www.cribfb.com/journal/index.php/BJMSR       Bangladesh Journal of Multidisciplinary Scientific Research         Vol. 1, No. 2; 2019 

59 
               

5. Conclusion 
The delay experienced by aircraft out of every schedule is high, because most aircraft don’t takeoff at the scheduled time which 
might be as a result of disruption. Aircraft damage, Bad weather, aircraft Maintenance, Aircraft fuelling, Congestion of the Airspace, 
Flight Documentation, Late Boarding, Baggage handling and VIP movement with the aid of Factor Analysis these factors were 
categorized into 3 components; Aircraft causes, Processing and Customer Factor which implies that the first was noted to be the 
major cause of delay. In addition, all contingencies must be incorporated into the schedule plan to give allowances for all forms of 
disruptions. 

The research recommends that Schedule disruption has impact on the turnaround operation considering the effects on 
the overall performance of Airport, Gates and Slots restriction, Operational restriction, Airports Restrictions and other factors 
make planning difficult but they must be considered in the process. Airlines should be realistic in their flight planning/schedules. 
Airlines should develop responsive crisis management systems and file their Operation Disruption Manuals with NCAA as 
required by law. Airlines should also adhere to the Nigerian Civil Aviation Regulations and Passengers Bill of    Rights in their 
dealings with passengers. This will motivate Passengers, by getting value for their money. 

Considering the factors causing delay, the resultant effect can be on passengers, consideration should be given in case of 
missed connection flight, so airlines to ensure they provide right to care to passengers in times of delays/cancellations. 
 
References 
Adeniran, A. O., and Kanyio, O. A. (2019). Quantitative model of single-server queue System. Indian Journal of Engineering, 16, 
177-183. 
Ayantoyinbo, B. (2015). Preferences for nigerian domestic passenger airline industry: a conjoint analysis. Journal of academia. 
Mike, H. (2008). The air transport system. England woohhead publishing limited. 
Mueller E.R and G. B. Chatterji. . (2002). Analysis of aircraft arrival and departure delay characteristics. Aiaa's aircraft technology, 

integration, and operations (atio), los angeles, california. 
Stephens, M. S., and Ukpere, W. I. (2011). Airport capacity utilization in nigeria: a performance and efficiency analysis. African 

journal of business management, pp. 11104-11115. 
Mohleji, S. (2001). Terminal airspace/airport congestion problems in us and fms/rnav applications to reduce delays. The mitre 

corporation , 9-10. 
Schaefer L, M. D. (2011). Flight delay propagation analysis with the detailed policy assessment tool. Proceedings of the 2001 ieee 

systems, man and cybernetics conference . 
Hansen, M. (2002). Micro-level analysis of airport delay externalities using deterministic queuing models: .a case study . . Journal 

of air transport management, 8 (2):73-87. 
Jianfeng, John F. Shortley; Juan wangz and Lance sherry. (2012). Analysis of gate-waiting delays at major us airports. Center for 

air transportation systems research, george mason university, fairfax, va, 22030, usa. 
Grigoriy, Y., Richard, S., Denis, C., John, F., Damon, L., & Manoj. (2014). Flight delay performance at hartsfield-jackson atlanta 

international. Journal of airline and airport management, 78-95. 
Trani, A. A. (2003). Review of airport runway capacity. Airport capcity . 
Wu, C. (2005). Inherent delays and operational reliability of airline schedules . Journal of air transport management, volume 11, 

issue 4, pages 273-282. 
Yufeng, T., Michael, B., & Wolfgang, J. (2005). Estimating flight departure delay distributions a statistical approach with long-

term trend and short-term pattern. Robert h. Smith school research paper no. Rhs 06-034. 
Yuqiong., B. (2001). Analysis of aircraft arrival delay and airport on-time performance. . Huazhong university of science and 

technology, china, 1-119. 
 

 
 
Copyrights  
Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-access 
article distributed under the terms and conditions of the Creative Commons Attribution license 
(http://creativecommons.org/licenses/by/4.0/). 
 


