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Long-Term Infrastructure Planning of Airport System in Developing Country

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Long-Term Infrastructure Planning of Airport System in Developing Country Long-Term Infrastructure Planning of Airport System in Developing Country Aprima Dheo Denisiano Technische Universiteit Delft Universiteit Technische Long-Term Infrastructure Planning of Airport System in Developing Countries By Aprima Dheo Denisiano 4513800 In partial fulfillment of the requirements for the degree of Master of Science in Transport, Infrastructure, and Logistics at the Delft University of Technology, To be defended publicly on Wednesday, 27 September 2017 at 09.00 AM Graduation Committee: Chairman: Prof. Dr. G.P. van Wee Full Professor, Department of Transport and Logistics Faculty of Technology, Policy, and Management First Supervisor: Dr. Milan Janic Department of Transport and Planning Faculty of Civil Engineering and Geosciences Second Supervisor: Dr. Jan Anne Annema Department of Transport and Logistics Faculty of Technology, Policy, and Management An electronic version of this thesis is available at http://repository.tudelft.nl/ Summary Air transportation is one of the vital systems in the world since it contributes enormous economic and social benefits. The development of airport as the key infrastructure which enables passengers to fly, needs a huge investment. Without a proper investment planning, the development will cause an enormous sunk cost and other consequential problems such as the failure with airport development of Mattala Rajapaksa International Airport in Sri Lanka. Thus, planning an airport—a new airport or expansion of existing airport—is a real challenge. In developing an Airport Master Plan (AMP), a guideline from IATA, FAA or ICAO can be used by airport operator or designer. These standard guidelines merely provide the lists of elements which need to be provided. Nonetheless, they do not equip the users with a calculation method to determine the ideal numbers (e.g., the number of runways). The process of determining the amount or size of infrastructure required in the master plan is specifically called airport infrastructure planning. By having the plan, an airport operator can prepare the budget to fund the expansion project. The previous study which conducted similar problem is the one in India. This study proposed the long-term plans of future investment in airport system. However, the passenger demand model, which only utilizes the relationship with GDP, is deemed to be insufficient. Moreover, the result is presented in the amount of investment required, not in specific value or unit of the infrastructure. The problem of estimating the future infrastructure is also faced by Angkasa Pura II (AP2) which manages 13 airports in Indonesia. Currently, most of the airports handled traffic more than its stated capacity. Per 2015, many airports in AP2 suffer from the over-capacity situation. The occupancy level of 7 out of 13 airports was in the over-capacity situation. In response to this condition, AP2 need to find the right amount of infrastructure to be built by 2030. However, the problems are the lack of performance data, i.e., delay data and lack of capability to assess the infrastructure utilization. Hence, the convenient method to calculate the future airport infrastructure in strategic level with limited input is needed. By having such method, the airport operators or airport authorities, especially in developing countries, can plan their strategic infrastructure expansion in an accountable way and independently. Accordingly, this research aims at helping airport operator to make a convenient method for estimating the required infrastructure to balance the future demand. The main research question of this study is: What is the convenient method for an airport system owner in developing countries to assess the infrastructure requirement of the airports for balancing the future demand considering the uncertainties in the future? First, the literature review is conducted to answer the research question. The econometric model is found popular for analyzing the airport demand since this model has underlying factors that have a relationship with airport demand. Many literatures attempt to explain the airport demand evolution with various explanatory variables. The variables can be categorized as economic activity, demographic structure, and tourism-related indicators. The common model structures found in the literature are the linear model and log-log model. The infrastructure design parameter that are estimated in the model are the number of runways, aircraft parking stands in apron-gate complex and passenger terminal size. The analytical models to calculate are found. A comprehensive uncertainty analysis on an airport is found. The uncertainties can be classified into three categories: (1) uncertainties affecting airport demand, (2) uncertainties affecting airport capacity, and (3) uncertainties affecting both, demand and capacity. The results from literature review is then combined into a method to estimate the required infrastructure in the future. It consists of two main models, the demand model, and the infrastructure model. The goal of demand model is to find the best model to explain the growth of airport traffic. Therefore, the model can be used to calculate the future demand. The infrastructure model, on the other hand, use the result of demand model to estimate the appropriate infrastructure to balance the demand. In the last step, the i uncertainty is explored and the scenario analysis is conducted to test how robust the output of the model is. The final recommendation is made based on the result of this scenario analysis. Application to the case of AP2 The proposed method then applied to AP2 case. The general demand is developed for all airports. From the demand model, it is found that GDRP, Population, and Domestic Tourist can explain the number of domestic passengers. The number of international passengers is explained by GDRP, Population, Export, Import and International Tourist. The dummy variables which explain hub position for the two airports, CGK and KNO, have a significant contribution to the passenger number. Then, demand models for each airport are developed as the general model has poor forecasting accuracy. After estimating the demand model for each airport using four possible configuration, the linear model formulation which includes all proposed explanatory variables is the best model to forecast the future demand of AP2 based on the average MAPE compared to the log-log model. Then, the future passenger number is forecasted using the models. In the base case, the total passenger handled by AP2 in 2030 is 174,757,647 passengers from 83,151,110 passengers in 2015. It means the traffic grows 210% for 15 years or equivalent to 5.08% per year. In the base case, the required number of runways should be added by 2, the number of parking stands should be 17 stands and passenger terminal should be expanded by 411,980 m2. The plausible uncertainty that may affect the plan is also analyzed. There are various uncertainties which might play a role in system of airports. The uncertainty considered in Schiphol Airport is taken as point of departure in analyzing the uncertainty in AP2. It is found that the uncertainties that affect airport operator may differ from the others. The scale of operation of the airport operator and the location of the airports are the sources of this difference. From these uncertainties, several scenarios are made to test the robustness of the recommendation. The result shows that the required infrastructure is different from the base case. Knowing that the uncertainty can alter the infrastructure estimation, we consider giving a range of plausible outcome rather than a single number. Hence, the final recommendation for AP2 is summarized in the following table. Additional parking Additional passenger terminal Additional runway1 Airport stands2 size3 Code Without With Without With Without With Uncertainty Uncertainty Uncertainty Uncertainty Uncertainty Uncertainty CGK 1 0 - 3 0 - 83 105,892 0 – 394,939 KNO 0 - 1 0 - 3 0 – 38,858 HLP - - 5,830 2,865 – 14,209 PLM - - 21,109 10,016 – 39,944 PNK - - 13,876 8,846 – 27,661 PDG - 2 0 - 6 33,048 16,468 – 55,591 PKU - - 37,187 26,830 – 53,306 BDO 1 1 - 1 15 12 - 22 135,848 120,180 – 180,020 BTJ - - 4,178 2,948 – 8,118 TNJ - - 3,290 1,975 – 5,441 PGK - - 26,949 18,212 – 39,405 DJB - 0 - 2 16,227 4,173 – 31,854 DTB - - 8,546 7,406 – 10,273 Total 2 1 - 5 17 12 - 116 411,980 219,919 – 899,619 Notes: 1) The additional runway is in parallel and have enough distance to achieve maximum capacity 2) The parking stands can be as gate or remote 3) The passenger terminal size is in m2 ii This range of recommendation shows that the best value is hard to determine when uncertainty analysis is added in estimating airport infrastructure. Therefore, the decision makers can use this information to make a detailed plan, but they should also include flexibility in the design within the master plan. For example, the development can take place in huge area but conducted in several modular infrastructures (gradual construction). Thus, in the execution, before the next module is constructed, the decision makers can reevaluate current demand situation and budget availability to decide whether the construction project can proceed at that year or not. To conclude, this study shows that for the airport operator in the developing countries with a lack of detailed data can use a relatively simple method to estimate the required airport infrastructures. The proposed method uses a combination of simple models: the demand model using a multiple linear regression, infrastructure sizing model which calculates the number of runway, the number of aircraft parking stands, and the size of passenger terminal. It should be kept in mind that the method also takes uncertainties into account by using scenario analysis which result in in a range of plausible required infrastructure in each airport. The method developed in this thesis is in principle the same as the one in a developed country.
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