A concept of forecasting origin-destination air passenger demand between global city pairs using future socio-economic scenarios
TL;DR: A concept of a new method of forecasting air passenger flows on a global level using socio-economic scenarios and predicting the number of passengers on existing and new connections is introduced.
Abstract: 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.
Summary (2 min read)
- American Institute of Aeronautics and Astronautics 1.
- The non-CO2 emissions have different impacts on the environment in different regions of the world.
- 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.
B. Academic studies
- The Aviation Integrated Modelling (AIM) 10,11 project was initiated by the University of Cambridge, UK.
- 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.
- Currently, this module contains a simple model on city level which considers realized undirected air passenger demand.
- 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 equation has been calibrated on current and historical data.
- The studies mentioned above have utilized a range of techniques and considered various levels of aggregation.
- In AIRCAST, 2012 has been adopted as the base year.
- American Institute of Aeronautics and Astronautics 5.
- 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 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).
A. Topology forecast
- The socio-economic characteristics of the cities change throughout the duration of the scenario.
- Many studies of network theory have been dedicated to link prediction, and these methods can be applied to model the evolution of the network.
- There are two groups of similarities: structural similarity and node attribute similarity.
- It is assumed that ranked existing links with a lower score, in a given year, are eliminated from the network.
- Thus, for calculating OD air passenger demand network topology, nodes , edges (OD city-pairs), weights (set of socio-economic indicators) and city communities are considered.
B. Passenger forecast
- The forecast of origin-destination air passenger demand between global city pairs consists of a sequential set of discrete "slices" at the time scale up to the forecast horizon.
- Thus, estimation of the number of air travel passengers on city-pairs has two steps: first, the defining of the number of passengers based on socioeconomic indicators and, second, the recalculation of the number of passengers, based on ATS-specific feedback information (Fig. 6).
- This process reveals the changes over time of city distributions within the clusters.
- For every time slice in the study, scores for all possible connections (9,832,395 connections in one slice) have been obtained.
- Preliminary topology forecasting has been made for each 5-year time slice.
- The authors would like to thank Antony Evans from the University College London and colleagues in the DLR Air Transport Systems for valuable comments and helpful discussions.
- 12 Reynolds, G.T. et al., “Modeling Environmental & Economic Impacts of Aviation: Introducing the Aviation Integrated Modelling Project”, 7th AIAA Aviation Technology, Integration and Operations Conference, Belfast, 2007.
- American Institute of Aeronautics and Astronautics 12 26 Zheleva, E., Golbeck, J., Kuter, U., “Using Friendship Ties and Family Circles for Link Prediction”, Advances in Social Network Mining and Analysis Lecture Notes in Computer Science, Vol. 5498, 2012, pp. 97-113. 27 Lü, L., Zhou, T., “Link prediction in weighted networks:.
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Cites background from "A concept of forecasting origin-des..."
...As a starting point passenger demand networks are directly initialized from exogenous socio-economic scenarios [54,55]....
Cites background or methods from "A concept of forecasting origin-des..."
...Detailed definitions of demand in this sense are given in ....
...Eventually, these distributions need to be compared to the sophisticated approach presented in ....
...For verification and validation, the historical trend and a projection need to be compared to the connections predicted by topology prediction module of the passenger demand forecast model (D-CAST) ....
...The subsequent demand prediction model, named D-CAST as described in  requires scenario information of socio-economic factors on city level to predict the realized demand worldwide from origin to destination on city level....
Cites methods from "A concept of forecasting origin-des..."
...A detailed approach to the applied demand forecasting methodology can be found in  and ....
...By demand here, we mean actual realized demand as described in , since our forecasting algorithms are based on global ADI-data1, which may be interpreted as realized demand....
...The following dependence was proposed in :...
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This study introduces a concept of a new method of forecasting air passenger flows on a global level using socio-economic scenarios. Preliminary calculations show promising results.