American Institute of Aeronautics and Astronautics
1
A concept of forecasting origin-destination air passenger
demand between global city pairs using future socio-
economic scenarios
Ivan Terekhov
*
, Robin Ghosh
†
and Volker Gollnick
‡
German Aerospace Center (DLR), Air Transportation Systems, Blohmstr. 18, Hamburg, 21079, Germany
This study introduces a concept of a new method of forecasting air passenger flows on a
global level using socio-economic scenarios. The method has two steps: forecasting the
topology of origin-destination demand network and predicting the number of passengers on
existing and new connections. Network theory is applied to simulate demand connections
between cities utilizing weighted similarity based algorithms. The number of passengers on a
connection is defined using quantitative analogies. Preliminary calculations show promising
results. This concept of the global passenger demand prediction will be applied in a modular
environment modeling the future air transport system.
I. Introduction
ir transport system (ATS) is a multi-disciplinary complex system with various interactions between
stakeholders within the system and its environment. The “atomic model” of the ATS in Fig.1, introduced by
Ghosh
1
, outlines the core stakeholders in the system, united by the aircraft as the major connecting element and
external elements. Changes in any element of this complex interconnected system may cause changes in the whole
system.
The ATS growth has a clear correlation to world economy growth. The number of worldwide passengers within
the ATS has increased from 1.5 to 3 billion from 2002 to 2013 (based on the ADI database
§
). Such growth has
undoubtedly an increasing affect on the environment. Airline schedules, network and fleet decisions are based on a
number of drivers, but arguably the most important is the available origin destination city-pair passenger demand. It
is likely that there will be a number of cities with significant air traffic connections that have no air traffic
connections today. This is particularly true for countries such as China. By not including air traffic to these growing
cities, global growth in air traffic and emissions would be underestimated, resulting in the corresponding
underestimate of the climate impacts associated with aviation. Therefore, there is the particular importance to
capture the full environmental impact of future growth in the aviation sector. As shown by Lee
2
, air transport
contributes 2-3% of global CO
2
emissions and 3.5-4.9% of global radiative forcing, if non-CO
2
effects are included.
Today, the impacts of CO
2
emissions on the environment are closely studied. However, non-CO
2
effects have not
been subject to the same level of study. Accordingly, a more robust scientific understanding of the effects of non-
CO
2
emissions is still needed
3
. The non-CO
2
emissions have different impacts on the environment in different
regions of the world. For example, NOx has been shown to induce short-lived greenhouse gas ozone. The gas
produced at the equator has a higher radiative forcing than the same amount of emissions in northern regions. This
implies that geographical information of a flight route, such as location of departure and destination airports, as well
as flight path, is essential for assessing the impact of non-CO
2
emissions. Accordingly, to assess the non-CO
2
impact, the number of flights and type of aircraft operated on routes must be known so as to quantify the amount of
such emissions on a global scale. To obtain this information the number of passengers on these routes must be
estimated. Finally, to make such estimations, air passenger demand between origin-destination has to be determined.
*
PhD candidate, Department System Analysis Air Transport.
†
Research Engineer, Department System Analysis Air Transport.
‡
Head of the DLR Air Transportation Systems.
§
Sabre Aviation Data Intelligence
A
American Institute of Aeronautics and Astronautics
2
This assessment approach of non-CO
2
emissions takes into account different layers of the ATS starting from
origin-destination air passenger demand to trajectories of an aircraft. Thus, there is a need for a methodology that is
capable to describe the interactions between elements and changes in the ATS. The German Aerospace Center
(DLR) project known as “WeCare”
**
studies the potential of climate efficient flight by using forecast weather
information on a global scale up to a 2050 time horizon, in the context of the ATS. One of the WeCare project’s aim
is to assess the impact of non-CO2 emissions on a global scale. Within this project, DLR Air Transportation
Systems is developing a modular environment known as “AIRCAST”
4
(air travel forecast), which aims to forecast
future development of the ATS based on socio-economic scenarios. AIRCAST allows DLR to simulate a range of
possible outcomes for the future development of the ATS and assess, for example, the impact of new technology on
the number of demand passengers or the size and number of aircraft on particular routes. From this, a chain of
models for the future ATS has been developed (Fig.2). The demand forecast model of ‘origin-destination air travel
passenger demand between city-pairs’ called “D-CAST”, based on socio-economic scenario, is the first layer in a
**
WeCare - utilizing weather information for climate efficient and eco efficient future aviation.
Figure 1: ATS model
Operator
Airport
Manufact
urer
ATM
Aircraft
Air transport system
Technology
Environment
Economy
Politics
Legislation
Society
Customers
Figure 2: 4-layers approach
American Institute of Aeronautics and Astronautics
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chain of models within “AIRCAST”. Since one of the WeCare project’s aim is to assess the impact of non-CO2
there is a high importance of geographical information of flights. The demand forecast model has to take into
account not only the number of air passengers, but also the possibility of changes to the number origin-destination
demand pairs. These pairs are formed by passengers traveling by air between an origin and a destination, regardless
of any intermediate stops. To meet these requirements, the model has to include cities where at least one airport is
present. The model must take into account these worldwide cities and simulate air passenger demand connections
between them and the number of passengers on these connections within the forecast period.
This paper is organized as follows. Section 2 introduces the assumptions and definitions used in the study.
Section 3 provides an overview of related studies in this area. Section 4 describes the data sources utilized for the
study. Section 5 presents the concept for forecasting air passenger demand. Section 6 presents the preliminary
results of the study. Section 7 summarizes the progress of the study to date and gives an outline of the future
research.
II. Demand terminology
Given the complexity and the novelty of this study, it is important that the assumptions and terminology used are
clearly defined at the outset. The first assumption is that every person in the world has a latent demand to travel by
air. The second assumption is that a particular set of individual conditions is required to give rise to a decision of a
person to travel by air. In other words, if these certain personal conditions are met, a person will choose to travel by
air. Such conditions could include socio-economic indicators (e.g. GDP, population, oil price, etc.) as well as ATS
specific indicators (e.g. travel time, frequency, number of transfers, airfare, etc.). Based on these assumptions two
categories can be defined: people whose conditions to travel by air are met constitute the realized demand; all other
people whose conditions to travel by air are not met constitute the unrealized demand. Accordingly, the sum of
realized and unrealized demand will make up the total demand for air travel. In other words, the sum of realized and
unrealized demand is equal to the latent demand. According to the first assumption, the latent demand is the world
population. For example, unrealized demand implies that a person does not travel by air due to various combinations
of reasons (e.g. high ticket price, long journey time, etc.). In opposite, realized demand implies that a person does
travel by air because individual and ATS-specific requirements of the person (e.g. ticket price, journey time,
itinerary, etc.) are satisfied.
In addition, directed air passenger demand is the demand in direction from point A to point B, but does not
include the demand in direction from point B to A. The sum of directed air passenger demand on directions A-B and
B-A represent the undirected air passenger demand between points A and B. In other words, undirected demand is
the total air passenger demand between two points regardless of directions.
III. Literature review
Forecast of air passenger demand is an important basis for planning in the constantly changing aviation
transportation system. The aircraft industry, and researchers, study air passenger demand and develop forecast
models using various techniques and levels of aggregation.
A. Industry forecasts
As discussed in Doucet et al. paper
5
, an origin destination air passenger demand model is an important part in the
Airbus Global Market Forecast (GMF)
6
methodology. The GMF methodology for forecasting future ATS contains
three basic steps: traffic forecast to the next 20 years, the network forecast
††
and a forecast of the number of required
aircraft. Air passenger demand forecast in the GMF is a part of the second step. For the network forecast, initially, a
traffic forecast between countries is disaggregated to a set of city pairs. Next, flight segments are modeled between
any two cities in the set. The obtained flight segments network includes existing routes as well as future possible
routes. Utilizing a market share model, a percentage of air passengers is assigned to each flight segment. Finally, the
number of passengers is defined as the percentage of passengers on each flight multiplied by the origin-destination
demand between cities. The origin-destination air passenger demand model utilizes a modified gravity model to
forecast the number of passengers between 279 cities around the world. The modified gravity model takes into
account a spatial dependence between origin and destination. In other words, the model takes into account the
impact to air passengers flow between cities by utilizing characteristics at proximal cities. From the perspective of
the present study, and its categorization of realized and unrealized demand, the air passenger demand model
discussed in Ref. 4, is dealing with realized directed air passenger demand.
††
Here „network forecast“ implies a forecast of routes between cities
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The United Kingdom Department for Transport’s UK Aviation Forecasts 2013
7
includes the National Air
Passenger Demand Model. This model uses a combination of a set of time series econometric models of past UK air
passenger demand including projections of key driving variables and assumptions about how the relationship
between UK air travel and its key drivers will change into the future. The model provides forecasts for domestic
destinations within the UK, international regions of origin for flights into to the UK and international passengers
connecting through UK airports.
Other industry forecasts are mainly predicting Revenue Passengers Kilometers (RPK). These forecasts do not
present a separate air passengers demand model. The Boeing Current Market Outlook 2013-2032
8
forecast uses an
empirical equation where RPK growth between regions is equal to sum of GDP growth and a time-varying function.
The function is not directly associated with GDP growth. This component of growth derives from the value travelers
place on the speed and convenience that only air travel can offer. In the Worldwide Market Forecast for Commercial
Air Transport
9
the Marketing Japan Aircraft Development Corporation developed a traffic forecast that predicts
RPK between 11 world regions. The relationship between past RPK, GDP and “Yield” is analyzed by each region to
obtain their regression equation, however the equation is not provided in their publication.
B. Academic studies
The Aviation Integrated Modelling (AIM)
10
,
11
project was initiated by the University of Cambridge, UK. The
aim of this project is to develop a tool to assess different current and future policies in aviation
12
. The AIM project
contains a set of connected modules that were created to fulfill the policy assessment goals of the project. These
modules are Aircraft Technology & Cost Module, Air Transport Demand Module, Airport Activity Model, Aircraft
Movement Module, Global Climate Module, Local Air Quality & Noise Module, and Regional Economic Module.
The Air Transport Demand Module deals with true origin-destination (OD) air passenger and freight demand.
Currently, this module contains a simple model on city level which considers realized undirected air passenger
demand. The model is represented as a gravity model with OD connections between 700 cities around the world.
The main variables in the gravity equation are: average local per capita income, greater metropolitan area or
equivalent population and generalized cost to a passenger of air travel between cities. The gravity equation has been
adapted to short-haul, medium-haul and long-haul as well as for different regions. The equation has been calibrated
on current and historical data.
Suryani et al.
13
model air passenger demand and passenger terminal capacity expansion using a systems
dynamics approach. The study concentrates on single airport level. Their model predicts when an airport should
expand runway capacity, passenger terminal capacity and to determine the total airport area needed to meet future
demand. Alam and Karim
14
address the present condition of the air transportation system in Bangladesh. They
analyze the operation and level of service of the system, realized undirected demand and supply structure and the
network configuration. A stepwise multiple linear regression analysis, using a time series collected for five years,
was utilized to calculate total passenger trips per week along existing routes. Grosche et al.
15
present gravity models
for the estimation of air passenger volume between city-pairs. The estimation is based on socio-economic and
geographic factors for the fixed number of city-pairs. Thus, this approach did not take into account the possibility of
new city-pairs within the air transport system.
C. Discussion
The studies mentioned above have utilized a range of techniques and considered various levels of aggregation.
Industry forecasts and academic studies show various methods to calculate the demand in particular airports
11
, on
particular routes
12, 13
, on regional level
6
or on city level with fixed number of connections between cities
9, 10
. Mostly,
the aforementioned forecasts deal with realized undirected air passenger demand using gravity models. Gravity
models have to be calibrated for different types of city-pairs (e.g. short-haul, medium-haul, long-haul, international,
regional, local, etc.). However, when dealing with larger numbers of city pairs, the complexity of the calibration
requirements of these models increases. Moreover, these studies do not include a method of forecasting an evolution
of air passenger demand between cities at a global level. They fail to take into account the potential for changes in
the number of airport-connected cities when forecasting demand within an air transport system.
IV. Data
In AIRCAST, 2012 has been adopted as the base year. For the base year, required data have been obtained from
Sabre Airport Data Intelligence (ADI) database: origin-destination city pairs worldwide, air passenger demand and
average airfare between these cities. Additionally, data for GDP, population and geographical coordinates of the
cities have been obtained from various databases (see below).
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The ADI database contains booking information from the Global Distribution System (GDS), its primary data
source, and other external data sources
16
. In the ADI database it is possible to obtain information of passenger
numbers between origin and destination airports as well as average airfare at OD level. The ADI database presents
the realized demand for air travel. Due to the assumption that air passenger demand is generated on city level and
not on airport level, the OD air passenger demand data from ADI has been aggregated to the city level. Thus, 4435
cities and 533170 connections between them have been obtained. In addition, for each city the GDP (utilizing UN
17
and World Bank
18
data), population (UN
19
and MaxMind
20
data) and geographical coordinates (OurAirports
21
and
OpenFlights
22
data) have been retrieved. The final database for the base year 2012 includes OD city pairs, socio-
economic indicators (city GDP, city population), geographical coordinates, great circle distances between OD cities
and average airfare between these cities. Various socio-economic scenarios could be used as alternative inputs for
forecasting air passenger demand. These scenarios contain annual data for GDP, population on city level and oil
price in a given period of time.
V. Method
This paper presents a concept to forecast the evolution of the air travel passenger demand between cities based
on socio-economic scenarios, taking into consideration the probability of changes to the number of the origin-
destination demand connections within the ATS over time. In other words, the proposed concept forecasts passenger
demand as well as topology changes of the ‘air passenger demand network’, within the forecast period. The method
computes air passenger demand at any given point of time within the forecast period. Within this study the demand
forecasting model considers realized undirected air passenger demand.
The method has two steps: forecasting the topology of the origin-destination demand network and calculating
demand on existing and new connections. The first step of the method, determines whether the demand connection
between a given city pair exists or not. This is done by implementing a weighted similarity-based algorithm. The
weight is represented by a combination of socio-economic information of cities in pairs, and the distance between
them. The second step of the method, based on the existence of air passenger demand between cities, seeks to
forecast the realized air passenger demand between these cities.
The method computes realized air passenger demand using a quantitative analogies method that takes into
account the socio-economic indicators of cities, the airfare information and geographical characteristics of given
city-pairs, at a given forecast period and for the base year (where all the required information about cities and the
connections is known). Next, the obtained number of passengers is calibrated by feedback information from routes,
aircraft movements and trajectory levels (see Fig. 3).
Figure 3: Set of models for forecasting the future ATS