Airport Demand 159 Forecasting for Long-Term Planning Roundtable Report Airport Demand 159 Forecasting for Long-Term Planning Roundtable Report This work is published under the responsibility of the Secretary-General of the OECD. The opinions expressed and arguments employed herein do not necessarily reflect the official views of OECD member countries. This document and any map included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. Please cite this publication as: OECD (2016), Airport Demand Forecasting for Long-Term Planning, ITF Round Tables, OECD Publishing, Paris. http://dx.doi.org/10.1787/9789282108024-en ISBN 978-92-82-10801-7 (print) ISBN 978-92-82-10802-4 (PDF) Series: ITF Round Tables ISSN 2074-3378 (print) ISSN 2074-336X (online) The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law. Photo credits: Cover © G. Tipene/shutterstock.com. 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This document and any map included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area TABLE OF CONTENTS – 5 Table of contents Executive summary ...................................................................................................................... 7 Chapter 1. Summary of discussions ......................................................................................... 11 Introduction .............................................................................................................................. 12 Overview of emerging findings ................................................................................................ 12 Improving forecasts of airport demand .................................................................................... 18 Choosing the forecasting approach .......................................................................................... 19 Getting the best out of the models used ................................................................................... 25 Dealing with risk and uncertainty in airport demand forecasts ............................................... 28 Recognising and quantifying risk and uncertainty ................................................................... 29 Airport demand forecasts and airline network development .................................................... 32 Developing and assessing risk management measures ............................................................ 39 Note .......................................................................................................................................... 45 References ................................................................................................................................ 46 Chapter 2. Addressing uncertainty in airport forecasting and planning ............................. 49 Introduction .............................................................................................................................. 50 Sources of uncertainty and risk ................................................................................................ 50 Examples of the impact of uncertainty on airports ................................................................... 51 Case study 1: Hungary/Malév .................................................................................................. 51 Case study 2: Zurich Airport and Brussels Airport .................................................................. 52 Case study 3: Lambert-St. Louis International Airport ............................................................ 53 Case study 4: Bellingham International Airport ....................................................................... 55 Methods for better addressing uncertainty in forecasting and planning ................................... 56 Framework and methodology ................................................................................................... 58 Step 1: Identify and quantify risk and uncertainty ................................................................... 60 Step 2: Assess cumulative impacts ........................................................................................... 63 Step 3: Identify risk response strategies ................................................................................... 67 Step 4: Evaluate risk response strategies .................................................................................. 69 Step 5: Risk tracking and evaluation ........................................................................................ 70 Conclusions .............................................................................................................................. 71 References ................................................................................................................................ 72 Chapter 3. Choice models and contemporary airport demand forecasting ......................... 73 Introduction .............................................................................................................................. 74 Forecasting in a rapidly changing market for aviation ............................................................. 74 Model capabilities .................................................................................................................... 75 Data implications ...................................................................................................................... 77 The MKmetric model ............................................................................................................... 78 Theoretical framework ............................................................................................................. 79 Reflection of the status quo as reference point and scenario simulation .................................. 85 Complexity and coverage ......................................................................................................... 87 Application of the KMmetric model ........................................................................................ 87 AIRPORT DEMAND FORECASTING FOR LONG-TERM PLANNING — © OECD/ITF 2016 6 – TABLE OF CONTENTS Airport choice ........................................................................................................................... 88 Route competitors .................................................................................................................... 89 Flight segments ........................................................................................................................ 90 Demand sensitivity to slots ...................................................................................................... 90 Dynamic catchment .................................................................................................................. 92 Long-term forecasts .................................................................................................................. 95 Conclusions .............................................................................................................................. 96 Notes ........................................................................................................................................ 98 References ................................................................................................................................ 99 Tables Table 1.1 Flexible airport expansion solutions for addressing uncertainty ....................... 42 Table 2.1 General risk response strategies to threats and opportunities ............................ 68 Figures Figure 1.1 Illustrative prediction intervals ........................................................................... 30 Figure 1.2 Decision tree for second Sydney airport ............................................................ 43 Figure 1.3 Example comparing probabilistic net present value ........................................... 44 Figure 2.1 Actual and forecast total passenger enplanements at Lambert-St. Louis International Airport ........................................................................................... 54 Figure 2.2 Actual and forecast total passenger enplanements at Bellingham International Airport ................................................................................................................ 56 Figure 2.3 Systems analysis framework .............................................................................. 59 Figure 2.4 Overview of elicitation and group aggregation techniques ................................ 60 Figure 2.5 Illustrative example of a summary plot of identified risks and uncertainties .... 61 Figure 2.6 Example of distribution fitting (using illustrative data) ..................................... 63 Figure 2.7 An illustration of the use of Monte Carlo simulation techniques to account for multiple sources of uncertainty .......................................................................... 65 Figure 2.8 Illustrative probability density and cumulative probability output from the Monte Carlo simulations .................................................................................... 66 Figure 2.9 Illustrative Tornado diagram .............................................................................. 66 Figure 2.10 Illustrative prediction intervals ........................................................................... 67 Figure 2.11 Decision tree for second Sydney airport ............................................................ 70 Figure 3.1 Consumers' complex alternative travel opportunities ......................................... 76 Figure 3.2 Sequence of models for air transport forecasting ............................................... 79 Figure 3.3 Spatial competition ............................................................................................. 80 Figure 3.4 Functional forms................................................................................................. 83 Figure 3.5 Validation, calibration and simulation ............................................................... 86 Figure 3.6 Airport choice for a trip from Bremen to Bangkok ............................................ 88 Figure 3.7 Route competitors for the route GRU - NRT ..................................................... 89 Figure 3.8 Passenger diversity on the flight segment HAM – DXB.................................... 90 Figure 3.9 Passenger demand sensitivity to the air service Seoul – Prague ........................ 91 Figure 3.10 Catchment of Frankfurt for destinations in the far east ...................................... 93 Figure 3.11 Catchment of Frankfurt for destinations in the British Isles .............................. 94 Figure 3.12 Competition between Lyon and Geneva for originating passengers .................. 95 Figure 3.13 Long term forecast Berlin (1993 to 2010) .......................................................... 96 AIRPORT DEMAND FORECASTING FOR LONG-TERM PLANNING — © OECD/ITF 2016 EXECUTIVE SUMMARY – 7 Executive summary Decisions on expanding airport capacity are often controversial. Good transport infrastructure is of key importance for productivity and economic growth but airport capacity that is highly accessible to central city areas means that residents suffer more noise, air pollution and landscape degradation. In addition, airport assets are often long-lived with long lead times. Forecasts of future demand are thus a key tool for the consideration of extra capacity as well as to measure the economic benefits and costs of this extra capacity. Air passenger markets are highly dynamic and strongly influenced by the regulatory environment. Markets that have been de-regulated have seen rapid growth as prices fell and new, low-cost business models emerged. Liberalisation also stimulated re-organisation of network services with concentration of demand on a few hub airports. Demand for air services increases rapidly as incomes rise, but the market is not homogenous and understanding the drivers of each market is critical. Long-term airport planning has to account for the risks entailed by these dynamics. This report reviews the state of the art in forecasting airport demand. It focuses particularly on addressing demand risk, passenger behaviour and uncertainty and discusses how to make more effective use of such analysis in planning decisions. Improving methodologies for air transport demand forecasting Demand forecasts for the medium to long term are judged to be essential to the successful planning and delivery of major airport infrastructure. A recent review of airport demand forecasts in the United States concluded that econometric modelling has been an effective tool for generating medium to long- term forecasts of airport activity (ACRP, 2007). However, the track record of airport demand forecasting appears to be mixed, with illustrations of successes, but also failures in terms of a large divergence between forecast and out-turn. The first priority area is therefore to improve forecasts of airport demand. The report puts forward four key recommendations: • Use quantitative methods to analyse the key drivers of airport demand Econometric and statistical analysis can help in understanding the key drivers of past growth trends and help to build forecasts of how the level of airport demand might develop in the future. Two kinds of quantitative methods are discussed in detail: − An approach based on the observed choices made by individual travellers (or groups), as between different possible destinations, modes, routes and airports and which uses this analysis as a basis for forecasting the likely development of future choices. Mandel (2014) presents a very comprehensive example of such an approach. That is, the econometric system approach developed by MKmetric to perform short and long-term air transport demand forecasts while considering various determinants such as socio- economy, policy, infrastructure and land use. − An alternative, non-econometric approach of behaviour modelling is the ‘Kenza’ approach which was initially developed for Airbus Industrie by Sallier (2010) and AIRPORT DEMAND FORECASTING FOR LONG-TERM PLANNING — © OECD/ITF 2016 EXECUTIVE SUMMARY – 8 represents, today, the primary short, medium and (very) long-term demand forecasting tool of Aéroports de Paris. • Use expert guidance to help interpret the quantitative results Expert assessment, perhaps formalised through techniques such as Delphi, can help to understand where and why the models’ analysis of the past might not fit the future and, in this way, to suggest appropriate modifications to the models’ forecasting results. • Quality-assure the analysis and counter the risks of optimism bias Quality assurance can help to improve the accuracy of forecasts and, equally important, assure stakeholders that the forecasts are unbiased, constraining the risks of optimism bias. • Reflect the risks and uncertainties that arise in even the best forecasts Recognising risks and uncertainties can help to develop better investment strategies, which aim to control adverse risks and to reduce their negative impact in order to improve the overall efficiency and added value of infrastructure investment. An important example that is discussed in detail is the risk that the dynamics in airline network developments pose to airports. Scenario analysis can be effective approach to explore and exemplify the impact of these risks if it is conducted properly and with expert guidance. Make better use of demand forecasts in airport infrastructure planning The second priority area is to make better use of demand forecasts in airport infrastructure planning. InterVISTAS (2014) examined how risk and uncertainty can be addressed in airport traffic forecasting and airport planning. The research developed a unified systems analysis framework that enables airport activity forecasters to identify risk factors, to understand the extent to which each risk factor introduces uncertainty into activity forecasts, and to ascertain how the risks and uncertainties are likely to interact so as to examine realistically their combined implications for air traffic going forward. The main purpose of recognising and quantifying risks and uncertainties in future airport demand is to help to develop useful risk management measures. Approaches divide into two broad groups: • Involve risk sharing The aim here is to facilitate the control and/or diversification of risk, in particular, through vertical integration. Examples include: − Long-term contracts (between airline(s) and airport), which have been used to manage some of the demand side risks to the development of major infrastructure by providing some control over the risks of asset stranding, at airports in a competitive market setting. − Probabilistic forecasts which have been employed at Aéroports de Paris since 2003 for determining optimal points for new capacity, in terms of revenues and cost. This provides the information required for informed negotiation with airlines that request increased capacity. When demand appears insufficient from the airport’s point of view airlines can be asked to pay a risk premium if they want the airport to build early. − Co-financing airport development. • Flexible strategic/dynamic strategic/adaptive planning The aim of a flexible approach to infrastructure investment is to reduce the costs of unexpected traffic outcomes. Examples include: AIRPORT DEMAND FORECASTING FOR LONG-TERM PLANNING — © OECD/ITF 2016