The term overbooking is usually related with the reservation system of an airline, which means booking a number of passengers than the offering capacity of the aircraft, to minimize the effect of no-show passengers percentage, as this no-show is in the last minute before takeoff of the flight means losing a secure revenue to be earned and losing seats that can be utilized or resold for that same flight, so the process is a balancing between the two terms overbooking and no-show, the income of the first compensate the lose caused by the second.

1. 16 study
Mohammed Salem Awad
PhD Candidature
Aviation Management - India
Spill Passengers Analysis
T
he term overbooking is usually related with the reservation system of an airline, which means booking a number of passengers
than the offering capacity of the aircraft, to minimize the effect of no-show passengers percentage, as this no-show is in the last
minute before takeoff of the flight means losing a secure revenue to be earned and losing seats that can be utilized or resold for
that same flight, so the process is a balancing between the two terms overbooking and no-show, the income of the first compensate
the lose caused by the second, consequently this process take a lot of time ,effort and cost some time have bad impact on the brand
name of the airline as the denied boarding passengers don’t accept the compensation offered by the airline, really the issue is how
to find the right overbooking policy that match the no-show rate. Many international consulting companies and aircraft manufacture
companies as Boeing Group try to solve this problem by using statistical inference, to define and control the assign route to right capacity
of aircraft, and usually Normal distribution with its parameters is used to control the passenger spill rate of the airline network.
Introduction:
The typical definition of spill passengers is the rate of
passengers per flight that exceed the number of proposed
seat / capacity of aircraft. So the passengers can be collected
based on daily, weekly, monthly, and annually. While these
data collection can be per flight, or many flights either one 120%
type or many types, as this analysis is used since 20 years.
The main concept of spill analysis is to defined and 100%
represents the demand function by statistical distribution Truncating Capacity
usually Normal distribution, and the importance of this 80%
distribution is to reflect the load factor of aircraft, and
Fill Rate
accordingly the impact of load factor is to map and 60%
mean demand
utilize the capacity of the aircraft to minimize the effect
of spill passengers, while the term spill is cognately 40%
Spill Area
used with statistical parameters of the distribution used,
20%
and which indicate the capacity offered and the market
demand by overbooking rate, and that recall to name
0%
the analysis by Filling instead of Spill Graph (1). 0 20 40 60 80 100 120 140
So the load factor is an important tool to define the right Seat Count
capacity to use in the respect network. Which consequently
define the extra passengers that need an overbooking policy. Graph (1) Fill and Spill Rate

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45 1.8
Normal Distribution 40 1.6
Theoretical Model and Its Application
One of the most useful distributions in practice, it 35 1.4
has a symmetrical shape, about his mean, taking 30 1.2
the shape of bell, and it can be represented by
25 1
20 0.8
B 738
15 0.6
A 310 A 330
z Normalize distribution 10 0.4
μ Mean
σ
5 0.2
Standard Deviation
0 0
50 70 90 110 130 150 170 190 210 230 250 270 290 310 330 350 370 390
Spill Model for Airlines: Graph (2) SAH-DXB Spill Analysis
Usually the following Model is used
Results:
The right capacity for sector SAH-DXB is 190 and the right
Where: type is A310 -200 with 200 seat as shown in table (2).
μ Mean of Number of Passengers in the analysis
σ Standard Division of Number of Passengers in the analysis Aircraft Demand Factor Load Factor Spill
K Standard Division of Demand/Mean of Demand B738 (154) 0.52 0.51 1.58
f0 Probability Density Function. A310 (200) 0.40 0.40 0.14
A330 (277) 0.29 0.29 0.00
F0 Cumulative Probability Density Function.
Optimum (190) 0.40 0.40 0.25
L Load Factor
Table No. (2) All Sectors – Optimum Situation
D Demand Factor
Case Study:
Sector SAH-DXB Input:
Table No. (3) All Sectors – Optimum Situation
Summary:
The analysis shows the possibility of defining the right capacity
Table No. (1) Input Data of aircraft for the right routes for point to point model, and
consequently the data are examined and represented by
Considering the traffic passengers for sector two parameters, as Mean and Standard Division of Normal
SAH-DXB Flt No. IY864 – table no. (1) Distribution. So that we can mapping a results as Load Factor,
By Calculation Demand Factor and spill passengers curve, when we are used
Mean: 81 improper type of aircraft for a specified route, and as it is indicated
Standard Deviation: 49.9 by the graph, and its related statistical analysis for Aircraft types as
And implementing the formula of Normal Distribution A330-200, A310-300, and B737-800. …… this can represented by
the following Graph is plotted graph (2) the optimum situation of utilizing the Fleet Capacity in table (3).■

3. Spill Passengers Analysis
A day in Hadramout
The Passenger Experience
Civil Aviation & Meteorology Authority, April - September 2011, issue 10, 11
OpenSkies
Reality and Ambition
www.camamagazine.com